Enterprise AI Attack Chains: Prompt Injection, Tool Abuse, Detection
Technical analysis of enterprise AI attack chains: indirect prompt injection, memory poisoning, tool abuse, MCP exposure, and detection.
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AI Threat Modeling: Trust Boundaries for RAG, Agents, and MCP
Advanced guide to AI threat modeling for RAG, agents, and MCP, with trust boundaries, context provenance, and identity separation.
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Attacking Multi-Agent Systems: Rogue Agents and A2A
A practical AI Red Team guide to A2A reconnaissance, canary agent registration, agent-card spoofing, and delegation auditing in an authorized lab.
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MCP Security: Tools and Permissions in Practice
MCP Security in practice: a safe lab with fictional documents, a mocked local agent, controlled tool exploitation, permissions, schemas, and auditable logs.
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Attacks on Embeddings and Vector Databases
Embedding attacks in practice: inversion, membership inference, semantic probing, evidence artifacts, and defenses for vector databases.
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AI Recon: LLMs, RAGs, and Agents
Authorized reconnaissance for enterprise AI: map LLMs, RAG pipelines, agents, tools, IAM, APIs, cloud exposure, and evidence without drifting out of scope.
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Autonomous Pentest Agent: AI, Red Team, and Attack Chains
Autonomous Pentest Agents can accelerate Red Team operations, but they also introduce risk around prompt injection, tool misuse, scope control, evidence quality, and detection.
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Complete Guide: How to Protect Apps from Prompt Injection
Prompt injection is not theoretical. It is a real vulnerability affecting AI applications through direct, indirect, multimodal, and RAG-based attack paths. This guide covers vectors, POCs, evasion techniques, testing, and defenses.
