CVE-2026-58484 (GCVE-0-2026-58484)
Vulnerability from cvelistv5 – Published: 2026-07-20 17:00 – Updated: 2026-07-20 18:52
VLAI
EPSS
VEX
Title
Network-AI: Poisoned environment backup manifest allows arbitrary recursive deletion during backup pruning
Summary
Network-AI is a TypeScript/Node.js multi-agent orchestrator. Prior to version 5.12.2, `EnvironmentManager.listBackups()` reads each backup's `_manifest.json` and trusts the manifest's `path` field. `EnvironmentManager.pruneBackups()` later passes that trusted `entry.path` directly to `rmSync(entry.path, { recursive: true, force: true })`. An attacker who can place or modify a manifest inside `data/<env>/.backups/<name>/_manifest.json` can cause `network-ai env backup prune --env <env> --keep <n>` or any code path invoking `pruneBackups()` to recursively delete an arbitrary path accessible to the Network-AI process user. This is fixed in v5.12.2. `pruneBackups()` no longer passes `entry.path` from the on-disk manifest to `rmSync`. The deletion path is recomputed from a format-validated `entry.backupId`, and a `dirname` containment check confines deletion to exactly one level under the backups directory. A poisoned manifest (e.g. `"path": "/"`) is now inert.
Severity
7.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-20 18:52 UTC
CWE
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/Jovancoding/Network-AI/securit… | x_refsource_CONFIRM |
| https://github.com/Jovancoding/Network-AI/commit/… | x_refsource_MISC |
| https://github.com/Jovancoding/Network-AI/release… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Jovancoding | Network-AI |
Affected:
< 5.12.2
|
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
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.
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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.
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.
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