CVE-2026-61539 turned attacker-influenced Llama tool output into Python RCE inside Xinference. This analysis traces the parser boundary, validates the primitive in a safe local lab, and maps durable detection and hardening controls.
MCP security in practice: the main Model Context Protocol risks, from indirect prompt injection to excessive privileges, and a practical hardening framework for agents, clients, and servers.
A practical AI Red Team guide to A2A reconnaissance, canary agent registration, agent-card spoofing, and delegation auditing in an authorized lab.
MCP Security in practice: a safe lab with fictional documents, a mocked local agent, controlled tool exploitation, permissions, schemas, and auditable logs.
Embedding attacks in practice: inversion, membership inference, semantic probing, evidence artifacts, and defenses for vector databases.
A practical AI Red Team guide for testing agents, RAG, memory, tools, and autonomy workflows beyond prompt injection, with checklists, metrics, and controls.
Authorized reconnaissance for enterprise AI: map LLMs, RAG pipelines, agents, tools, IAM, APIs, cloud exposure, and evidence without drifting out of scope.
Autonomous Pentest Agents can accelerate Red Team operations, but they also introduce risk around prompt injection, tool misuse, scope control, evidence quality, and detection.
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.
