Tagged:
owasp4 posts
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Least Privilege for AI Agents: Runtime Capability Minimization and Reducing Blast Radius
Most agent deployments give AI agents too many capabilities by default — violating a 50-year-old security principle. A concrete framework for enumerating required capabilities, scoping tool access by task, sandboxing execution environments, and auditing capability creep to reduce blast radius.
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LLM Security Monitoring in Production: Anomaly Detection, Audit Logging, and Intrusion Detection for AI Systems
Guardrails block known bad inputs. Incident response handles breaches after they happen. This post covers the gap: building LLM-specific observability that detects attacks while they are occurring — anomalous outputs, prompt injection signatures, unusual tool-call patterns, and model drift from baseline.
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Circuit Breakers for AI Agents: Designing Controllability, Action Budgets, and Emergency Stops
Your agent will eventually get confused. The question is whether it fails safely or catastrophically. Here are the engineering patterns — borrowed from distributed systems and adapted for AI agents — that make the difference.
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OWASP Top 10 for AI Agents, Part 1: The Three Vulnerabilities That Break Agent Trust
Agents aren't just chatbots with tools attached. They're autonomous systems that read, write, call APIs, and act with your credentials. This series builds an agent-specific security checklist from first principles, starting with the three vulnerability classes that the OWASP LLM Top 10 2025 elevated: Prompt Injection, Excessive Agency, and Insecure Tool Chaining.