Red-Team Your LLM: Attack Vectors & Evidence (September 2026)

LLM red teaming is presented as structured adversarial testing for behavioral, probabilistic, and context-dependent AI failures such as prompt injection, jailbreaking, sensitive-data disclosure, unsafe tool use, and retrieval poisoning, which traditional code-focused penetration testing may not detect. Effective testing must address the model, application, tool integration, and inter-agent communication layers, particularly in RAG and agentic systems where malicious retrieved content or compromised handoffs can influence downstream behavior. The text links documented red team findings, remediation actions, and version-specific regression tests to EU AI Act risk-management and robustness obligations, arguing that policy statements alone are insufficient audit evidence. It recommends a five-phase process covering threat modeling, attack planning, manual and automated test generation, documented scoring, and regression validation, supported by tools such as Garak, PyRIT, PromptFoo, and LLM-based attackers. It also describes Openlayer as a platform intended to convert findings into CI/CD deployment gates, runtime blocks or redactions, monitoring records, and audit trails, while emphasizing that human-led discovery and automated regression testing serve complementary roles.

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