CVE-2026-77294 (GCVE-0-2026-77294)
Vulnerability from cvelistv5 – Published: 2026-09-24 18:04 – Updated: 2026-09-24 18:35
VLAI
EPSS
VEX
Title
TREK: Server-Side Request Forgery via User-Configurable LLM Base URL
Summary
TREK is a collaborative travel planner. Prior to 3.3.0, TREK allows an authenticated user to store an attacker-controlled llm_base_url through the settings API when the LLM_PARSING feature is enabled. Write permission to the target trip instance is required to trigger the vulnerable AI-assisted import path. The value is consumed by the clients in server/src/nest/llm-parse/clients/openai-compatible.client.ts, server/src/nest/llm-parse/clients/anthropic.client.ts, and server/src/nest/llm-parse/router/ollama-format.client.ts without applying the server-side request forgery guard. Triggering AI-assisted trip parsing causes the server to request the supplied destination, and upstream error response text can be returned in parsing warnings. This permits internal service discovery and access to link-local cloud metadata, with possible disclosure of infrastructure credentials and subsequent modification of protected cloud resources. This issue is fixed in version 3.3.0.
Severity
8.1 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-24 18:35 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/liketrek/TREK/security/advisor… | x_refsource_CONFIRM |
| https://github.com/liketrek/TREK/pull/1520 | x_refsource_MISC |
| https://github.com/liketrek/TREK/commit/19064b391… | x_refsource_MISC |
| https://github.com/liketrek/TREK/releases/tag/v3.3.0 | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| mauriceboe | TREK |
Affected:
< 3.3.0
|
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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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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