Vulnerability-Lookup

GHSA-37FH-F35C-R73M

Vulnerability from github – Published: 2026-06-05 18:31 – Updated: 2026-06-08 21:31

Details

DataDog::DogStatsd versions through 0.07 for Perl allow metric injections from event tags.

DataDog::DogStatsd does not properly sanitise input, allowing metric injections of data from untrusted sources.

The format_event method (used by the event method) does not validate the content of the tags, which may contain commas (allowing tags to be injected) or newlines, pipes and colons that allow metric injections. (There is an ineffective s/|//g to remove pipes, but because the pipe is not escaped, it is interpreted as a regular expression metacharacter and has no effect.)

Severity

9.8 (Critical)


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

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2026-11362"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-93"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-06-05T16:16:41Z",
    "severity": "CRITICAL"
  },
  "details": "DataDog::DogStatsd versions through 0.07 for Perl allow metric injections from event tags.\n\nDataDog::DogStatsd does not properly sanitise input, allowing metric injections of data from untrusted sources.\n\nThe format_event method (used by the event method) does not validate the content of the tags, which may contain commas (allowing tags to be injected) or newlines, pipes and colons that allow metric injections.  (There is an ineffective s/|//g to remove pipes, but because the pipe is not escaped, it is interpreted as a regular expression metacharacter and has no effect.)",
  "id": "GHSA-37fh-f35c-r73m",
  "modified": "2026-06-08T21:31:49Z",
  "published": "2026-06-05T18:31:39Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-11362"
    },
    {
      "type": "WEB",
      "url": "https://www.cve.org/CVERecord?id=CVE-2026-46719"
    },
    {
      "type": "WEB",
      "url": "https://www.cve.org/CVERecord?id=CVE-2026-46720"
    },
    {
      "type": "WEB",
      "url": "https://www.cve.org/CVERecord?id=CVE-2026-46741"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}

CVE-2026-11362 (GCVE-0-2026-11362)

Vulnerability from cvelistv5 – Published: 2026-06-05 14:50 – Updated: 2026-06-08 18:20

Title

DataDog::DogStatsd versions through 0.07 for Perl allow metric injections from event tags

Summary

DataDog::DogStatsd versions through 0.07 for Perl allow metric injections from event tags. DataDog::DogStatsd does not properly sanitise input, allowing metric injections of data from untrusted sources. The format_event method (used by the event method) does not validate the content of the tags, which may contain commas (allowing tags to be injected) or newlines, pipes and colons that allow metric injections. (There is an ineffective s/|//g to remove pipes, but because the pipe is not escaped, it is interpreted as a regular expression metacharacter and has no effect.)

Severity

9.8 (Critical)


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

SSVC

Exploitation: none Automatable: yes Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-06-08 18:20 UTC

CWE

CWE-93 - Improper Neutralization of CRLF Sequences
CWE-150 - Improper Neutralization of Escape, Meta, or Control Sequences

Assigner

CPANSec redhat root

References

3 references

URL	Tags
https://www.cve.org/CVERecord?id=CVE-2026-46741
https://www.cve.org/CVERecord?id=CVE-2026-46719
https://www.cve.org/CVERecord?id=CVE-2026-46720

Impacted products

1 product

Vendor	Product	Version
BINARY	DataDog::DogStatsd	Affected: 0 , ≤ 0.07 (custom)

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "cvssV3_1": {
              "attackComplexity": "LOW",
              "attackVector": "NETWORK",
              "availabilityImpact": "HIGH",
              "baseScore": 9.8,
              "baseSeverity": "CRITICAL",
              "confidentialityImpact": "HIGH",
              "integrityImpact": "HIGH",
              "privilegesRequired": "NONE",
              "scope": "UNCHANGED",
              "userInteraction": "NONE",
              "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
              "version": "3.1"
            }
          },
          {
            "other": {
              "content": {
                "id": "CVE-2026-11362",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "yes"
                  },
                  {
                    "Technical Impact": "total"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2026-06-08T18:20:03.616117Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2026-06-08T18:20:09.533Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "collectionURL": "https://cpan.org/modules",
          "defaultStatus": "unaffected",
          "packageName": "DataDog-DogStatsd",
          "product": "DataDog::DogStatsd",
          "programRoutines": [
            {
              "name": "DataDog::DogStatsd::format_event"
            },
            {
              "name": "DataDog::DogStatsd::event"
            }
          ],
          "repo": "https://github.com/binary-com/dogstatsd-perl",
          "vendor": "BINARY",
          "versions": [
            {
              "lessThanOrEqual": "0.07",
              "status": "affected",
              "version": "0",
              "versionType": "custom"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "DataDog::DogStatsd versions through 0.07 for Perl allow metric injections from event tags.\n\nDataDog::DogStatsd does not properly sanitise input, allowing metric injections of data from untrusted sources.\n\nThe format_event method (used by the event method) does not validate the content of the tags, which may contain commas (allowing tags to be injected) or newlines, pipes and colons that allow metric injections.  (There is an ineffective s/|//g to remove pipes, but because the pipe is not escaped, it is interpreted as a regular expression metacharacter and has no effect.)"
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-93",
              "description": "CWE-93 Improper Neutralization of CRLF Sequences",
              "lang": "en",
              "type": "CWE"
            }
          ]
        },
        {
          "descriptions": [
            {
              "cweId": "CWE-150",
              "description": "CWE-150 Improper Neutralization of Escape, Meta, or Control Sequences",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2026-06-05T14:50:12.176Z",
        "orgId": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
        "shortName": "CPANSec"
      },
      "references": [
        {
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-46741"
        },
        {
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-46719"
        },
        {
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-46720"
        }
      ],
      "source": {
        "discovery": "UNKNOWN"
      },
      "title": "DataDog::DogStatsd versions through 0.07 for Perl allow metric injections from event tags",
      "workarounds": [
        {
          "lang": "en",
          "value": "Ensure that metric names, values and tags come from trusted sources or are properly sanitised."
        }
      ],
      "x_generator": {
        "engine": "cpansec-cna-tool 0.1"
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "9b29abf9-4ab0-4765-b253-1875cd9b441e",
    "assignerShortName": "CPANSec",
    "cveId": "CVE-2026-11362",
    "datePublished": "2026-06-05T14:50:12.176Z",
    "dateReserved": "2026-06-05T11:42:59.357Z",
    "dateUpdated": "2026-06-08T18:20:09.533Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.