CVE-2026-86775 (GCVE-0-2026-86775)
Vulnerability from cvelistv5 – Published: 2026-09-09 13:32 – Updated: 2026-09-14 13:33
VLAI
EPSS
VEX
Title
knowns before 0.30.0 Path Traversal via Document API
Summary
knowns (npm package) versions <= 0.29.1 contain a path traversal vulnerability in the Document API. The HTTP handler in internal/server/routes/docs.go normalizes the user-supplied document path with cleanDocPath(), which strips leading/trailing slashes and the .md suffix but does not neutralize ../ traversal sequences, and internal/storage/doc_store.go then builds the target path with filepath.Join(ds.docsDir(), filepath.FromSlash(doc.Path)+".md") without verifying that the resolved path remains inside the documents directory. In the default deployment, where the Management API is unauthenticated and bound to all interfaces, a remote unauthenticated attacker can supply a traversal payload (for example {"path": "../../../../tmp/knowns_pwn_marker"} to POST /api/docs, or an encoded path to GET /api/docs/...) to read, create, overwrite, or delete arbitrary files with a .md extension anywhere on the host filesystem and to create arbitrary directories via os.MkdirAll. This can expose sensitive data stored in other projects' documentation, corrupt or destroy files, and provide an arbitrary-write primitive that may be chained toward code execution. The issue is fixed in version 0.30.0.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-14 13:32 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/knowns-dev/knowns/security/adv… | vendor-advisory |
| https://www.vulncheck.com/advisories/knowns-befor… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| knowns-dev | knowns |
Affected:
0 , < 0.30.0
(semver)
Unaffected: 0.30.0 (semver) |
guessed |
Date Public
2026-08-14 00:00
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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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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.
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