using-tensor-compiler-engineering
Pass
Audited by Gen Agent Trust Hub on Aug 10, 2026
Risk Level: SAFEPROMPT_INJECTION
Full Analysis
- [PROMPT_INJECTION]: The skill is designed to ingest and process Intermediate Representations (IR) of tensor programs from various sources (DSLs, ONNX, etc.), as described in
ir-contracts-and-semantic-identity.md. This represents a surface for Indirect Prompt Injection where malicious logic could be embedded in the graph structure to influence the compiler's output or the agent's behavior. - Ingestion points: The 'Ingest' stage in
compiler-architecture-for-tensor-programs.mdprocesses external IR graphs. - Boundary markers: The skill emphasizes 'IR Contracts' and semantic hashes for identity preservation, but lacks explicit security boundaries to isolate or sanitize adversarial graph inputs.
- Capability inventory: The skill utilizes
torch.compile,torch.fx, andAOTAutogradto perform runtime code generation and execution of artifacts. - Sanitization: No security-focused sanitization of the graph structure is implemented beyond semantic validation.
- [COMMAND_EXECUTION]: The skill leverages the PyTorch compilation stack (Dynamo, AOTAutograd, Inductor) to perform dynamic code generation and execution, as detailed in
torch-compile-and-aotautograd.md. While these are standard tools for tensor compilation, they represent a significant capability tier that requires carefully controlled inputs.
Audit Metadata