AST01 of the OWASP Agentic Skills Top 10 dissected: how ClawHavoc shipped 1,184 malicious skills from 12 accounts, why five of ClawHub's top seven downloads were malware, and how three lines of markdown exfiltrated SSH keys - with the full attack playbook and controls.
The OWASP Agentic Skills Top 10 maps the 10 risks of the AI-agent skill ecosystem - malicious skills, supply chain compromise, over-privileged manifests, metadata attacks, weak isolation, update drift, scanning gaps, governance failures and cross-platform reuse - with real 2026 evidence.
RAG pipelines trust whatever lands in the vector database. Learn how corpus poisoning, retriever manipulation, and injected documents hijack AI answers — and the defenses that actually work in 2026.
How attackers hijack AI applications with nothing but text: direct vs indirect injection, real incidents from Bing to EchoLeak, why no complete fix exists, and the layered architecture that actually contains it.
Five years ago, application security meant securing APIs, patching web frameworks, and hunting for SQL injection in input fields. Today, we’re handing over SSH keys, database credentials, and deployment pipelines to AI agents that make their own decisions about what code to run. Post-Quantum Cryptography for Cyber Professionals Red Teaming LLM Applications: A Practical Playbook […]
Your IAM system answers 'who has access' — AI agents ask 'what will this identity do next.' The authority gap, three real attack scenarios, and a four-layer framework to close it.
How AI agents transform enterprise SOC operations — autonomous triage, incident response, threat hunting, and compliance automation.
How AI models get stolen through extraction and distillation attacks. Explore techniques, defenses, and real-world ML security case studies.
Master the three-layer model for AI agent identity: cryptographic identity, capability permissions, and runtime least privilege for autonomous AI systems.
Explore multi-agent AI security: A2A protocol hardening, MCP boundary enforcement, cross-agent memory isolation, and trust boundary design patterns.
Red teaming LLM applications requires fundamentally different techniques than traditional penetration testing. This playbook covers the complete methodology: reconnaissance, attack execution across 5 categories, advanced adversarial ML techniques, and a reporting framework for AI security assessments.
The OWASP Top 10 for Agentic Applications defines the most critical security risks for autonomous AI agents in 2026. From prompt injection and tool hijacking to supply chain poisoning and multi-agent attacks, this comprehensive guide covers every vulnerability with real attack scenarios and proven defense strategies.