AI Cyber Defense / Protecting AI systems used by defenders and the business
AI Cyber Defense: Keep model outputs out of shell execution
Expose narrow typed tools backed by fixed executables or APIs. Keep user and model text as arguments only where validated, and avoid general shell access.
Environment and inputs: Enterprise AI assistants, security copilots, retrieval systems and application-controlled tool executors; framework-independent design.
Scenario and goal
A generated explanation can become dangerous if an application interpolates it into a command.
Why it matters
This path concerns attacks against AI-enabled systems used by the defender or business. It complements defenses against attackers who use AI as a tool. Treat model inputs, retrieved evidence and generated plans as untrusted proposals at execution boundaries. A model may assist analysis, but the application remains responsible for tenant isolation, authorization, resource budgets and the integrity of approvals and receipts.
Evidence to collect
Collect proposed tool operation, schema-validation result, executable identity and argument boundaries without logging secrets.
Validate with an authorized exercise
Submit harmless shell-metacharacter fixtures to a test tool dispatcher; confirm they are rejected or handled as literal permitted data and cannot change the executed operation.
Rollout and checks
Use synthetic evidence and mocked write tools for adversarial evaluation. Version the model, prompts, tool contracts and retrieval configuration together. Keep high-impact permissions outside the model and retain an operator takeover path. When an evaluation fails, correct the enforceable boundary and rerun the same fixture rather than relying only on a stronger wording of the prompt.
Scope and limits
This guide proposes a defensive test; it is not a tested production design. Check platform support and local requirements. References explain the mechanisms but do not certify the design. Behavior alone does not prove AI involvement.