CVE-2026-67185 (GCVE-0-2026-67185)
Vulnerability from cvelistv5 – Published: 2026-07-28 16:30 – Updated: 2026-07-28 19:34 X_Open Source
VLAI
EPSS
VEX
Title
TinyWeb 0.0.8 Path Traversal via URL Path Component
Summary
TinyWeb through 0.0.8 contains a path traversal vulnerability that allows unauthenticated attackers to read arbitrary files by submitting ../ sequences in the URL path, which are concatenated directly to the configured web root in HttpBuilder::buildResponse() without normalization, dot-segment removal, or boundary checks. Attackers can craft a single request with ../ sequences that pass through the URL parser unchanged and reach the filesystem call via HttpFile::setFile(), exposing sensitive files such as credential stores and private keys when the server process runs as root.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-28 19:34 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/theopaid/Unauthenticated-Path-… | technical-descriptionexploit |
| https://www.vulncheck.com/advisories/tinyweb-path… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| GeneralSandman | TinyWeb |
Affected:
0b3b5fdb5a058f50248cd8547824936b8dd10351 , ≤ a381da252fe8e873c8aff22703040426cc9b2ae0
(git)
Affected: 0.0.8 (semver) |
guessed |
Date Public
2026-07-26 00:00
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Sightings
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
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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.