vllm-ascend-server
Fail
Audited by Gen Agent Trust Hub on Apr 18, 2026
Risk Level: HIGHCREDENTIALS_UNSAFECOMMAND_EXECUTIONREMOTE_CODE_EXECUTIONEXTERNAL_DOWNLOADSDATA_EXFILTRATION
Full Analysis
- [CREDENTIALS_UNSAFE]: The skill provides deployment templates using
sshpasswith hardcoded password placeholders (-p 'password'), which encourages insecure credential handling and increases the risk of password exposure in shell history or process listings. - [COMMAND_EXECUTION]: The workflow includes instructions for the agent to terminate arbitrary system processes via
kill -9based on user-provided or discovered PIDs, which can lead to accidental or malicious system instability. - [COMMAND_EXECUTION]: Several Docker deployment templates recommend the use of the
--privilegedflag, which bypasses container isolation and grants the container full access to the host system resources and devices. - [REMOTE_CODE_EXECUTION]: Multiple configuration files and launch templates explicitly enable the
--trust-remote-codeflag for vLLM. This setting allows the execution of arbitrary Python code embedded within model repositories or weight files, posing a significant risk if the model source is compromised. - [EXTERNAL_DOWNLOADS]: The skill instructs the agent to perform runtime installation of Python packages (
pip install vllm vllm-ascend) without version pinning or checksum verification, making it vulnerable to supply chain attacks or environment inconsistencies. - [DATA_EXFILTRATION]: The skill facilitates the transmission of commands and model configurations to remote servers via SSH, creating a pathway for sensitive data or model weights to be moved across network boundaries.
- [INDIRECT_PROMPT_INJECTION]: The skill automatically discovers and parses external configuration files (
config.json,quant_model_description.json) to dynamically generate shell commands for server deployment. This ingestion of untrusted data into command generation represents a potential injection vector. - Ingestion points: Found in Phase 2 (Model Discovery) where it searches for and reads
config.jsonfiles from paths like/home/weights. - Boundary markers: No delimiters or safety warnings are present to prevent malicious instructions inside these metadata files from influencing the generated shell commands.
- Capability inventory: The skill possesses capabilities for shell execution, Docker management (
docker run,docker exec), SSH communication, and process termination (kill -9). - Sanitization: No evidence of sanitization or validation of the values extracted from the model configuration files was observed before they are interpolated into execution strings.
Recommendations
- AI detected serious security threats
Audit Metadata