Security through simplicity
The OWASP AI Testing Guide structures evaluations for security, privacy, and compliance across AI implementations. Practical checklist Define abuse cases and measurable success criteria (jailbreak rate, leakage rate, task failure rate). Automate evals in CI/CD with seed prompts and benchmark suites; fail builds on thresholds. Run targeted red teaming on high-impact tasks; rotate model versions…
Use the AI Exchange’s control families to extend your ISMS. Align AI risks and controls to Annex A (2022) and make audits a byproduct of delivery. Quick mapping ideas Outcome: defensible operations and faster audits.
The OWASP ML Security Top 10 highlights attacks like input manipulation, data poisoning, model theft and insecure supply chains. What good looks like Dataset integrity: provenance checks, hash manifests, and differential privacy for sensitive features. Model artifact security: signed models, SBOMs for training pipelines, and private registries. Robustness testing: adversarial evals, drift detection, and red-team…
The OWASP Top 10 for LLM Applications calls out LLM01 Prompt Injection, LLM02 Insecure Output Handling, and LLM08 Excessive Agency as critical risks. Mitigations you can implement today Isolation & sandboxing for tool execution and code evaluation; never run model output with production privileges. Strong allow-lists for tools, file types and destinations; enforce typed tool…
The OWASP AI Exchange is a living body of guidance that consolidates threats and controls for AI systems across the lifecycle. It provides a vendor-neutral baseline that can be mapped into your policies, SDLC and runbooks. Key takeaways Design for abuse cases: Identify prompt injection, data leakage, model theft, and red-teaming requirements early. Control families:…