Vram-GPU-OOM
Fail
Audited by Gen Agent Trust Hub on Feb 16, 2026
Risk Level: HIGHEXTERNAL_DOWNLOADSDATA_EXFILTRATION
Full Analysis
- [EXTERNAL_DOWNLOADS] (MEDIUM): Reference to an external helper script from an untrusted source. The skill documentation points to
request_gpu_unload.pyin theOneCuriousRabbitrepository, which is not a verified or trusted source. - [DATA_EXFILTRATION] (LOW): Hardcoded internal network targets and SSRF risk. The signaling protocol example includes a hardcoded private IP address (
10.99.0.3). While intended for local coordination, this pattern presents a Server-Side Request Forgery (SSRF) surface if an agent or user dynamically populates the service list with untrusted URLs. - [INDIRECT_PROMPT_INJECTION] (LOW): Vulnerability surface through untrusted service responses. The signaling protocol ingests external data that influences agent/system logic.
- Ingestion points: Ingests JSON data from remote services via
resp.json()in theSERVICESloop. - Boundary markers: Absent; the code trusts the response structure from the remote endpoint.
- Capability inventory: Includes the ability to perform network POST requests and trigger model unloads (state changes).
- Sanitization: Absent; the logic directly processes keys like
unloadedandstatusfrom external JSON responses. - [FALSE POSITIVE]: The automated scan alert regarding
logger.infois a false positive. The string is part of standard Python logging:logger.info(f"Auto-unloading model after {idle/60:.1f} minutes")and does not represent a malicious URL.
Recommendations
- Contains 1 malicious URL(s) - DO NOT USE
Audit Metadata