CVE-2026-61684 (GCVE-0-2026-61684)
Vulnerability from cvelistv5 – Published: 2026-07-15 14:28 – Updated: 2026-07-15 15:07
VLAI
EPSS
VEX
Title
FastGPT: Unauthenticated cross-tenant data access via forgeable plugin-invoke JWT (default INVOKE_TOKEN_SECRET='token')
Summary
FastGPT is a knowledge-based AI application platform. In 4.15.0-beta4, FastGPT plugin invoke reverse-call endpoints under /api/invoke/* authenticate only by verifying a JWT signed with INVOKE_TOKEN_SECRET, which defaults to the constant string token and was not set in official deployment templates. An unauthenticated attacker can self-sign an HS256 JWT and reach /api/invoke/userInfo to disclose cross-tenant user PII by attacker-supplied tmbId values, or /api/invoke/fileUpload to write attacker-controlled content into chat files. This issue is fixed in version 4.15.0-beta5.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-15 15:07 UTC
CWE
- CWE-798 - Use of Hard-coded Credentials
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/labring/FastGPT/security/advis… | x_refsource_CONFIRM |
| https://github.com/labring/FastGPT/pull/7170 | x_refsource_MISC |
| https://github.com/labring/FastGPT/commit/f5f1e58… | x_refsource_MISC |
| https://github.com/labring/FastGPT/releases/tag/v… | x_refsource_MISC |
Impacted products
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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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.
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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.