TREK: Server-Side Request Forgery
CVE-2026-77294 is a high-severity server-side request forgery vulnerability in TREK from Mauriceboe, with a CVSS 3.1 base score of 8.1 out of 10. It can be exploited remotely over the network by an attacker with a low-privileged account, without any action from a user. Affected TREK versions: < 3.3.0. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.31% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of TREK you run against the affected versions below, and install the vendor's latest release.
- Read the advisory on github.com for workarounds and exact fixed builds.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Mauriceboe | TREK | < 3.3.0 | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- LowA basic user account is enough.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- HighData or code can be modified at will.
- Availability
- NoneAvailability is not affected.
Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N. Scored by CNA (github.com).
Official description
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.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Server-Side Request Forgery
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.
How developers prevent this class of flaw
- If there is a known list of applications, IP, hostname, and/or domains to which the product is allowed to communicate, then use allowlists to ensure that outgoing requests are restricted to these known entities. The allowlists could be in the application code or possibly enforced by firewalls or network segmentation.
- If the product is expected to be able to connect to arbitrary IP addresses or domains, consider using a denylist that includes private network addresses, internal domain names, etc. The denylist could be in the application code or possibly enforced by firewalls or network segmentation.
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: poc, automatable: no, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Patches and release notes
Other vulnerabilities in TREK
- 7.1 High severity
Recent Server-Side Request Forgery vulnerabilities
- 7.3 High severity
- 7.7 High severity
- 8.6 High severity
- 8.5 High severity
- 7.7 High severity
- 4.3 Medium severity
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