Compare 38 AI red teaming partners delivering adversarial testing, jailbreak discovery, prompt injection assessment, model evaluation, and safety review programmes for generative AI applications and agentic systems. Listings include OWASP LLM Top 10 coverage, NIST AI RMF alignment, EU AI Act readiness, vertical focus, and verified buyer ratings from AI safety and security teams. The market has matured rapidly through 2024 and 2025 as buyers shift from one-off engagements to standing red team retainers, particularly for agentic systems where attack surface grows with every tool integration. Use this directory to shortlist AI red team, evaluation, and ongoing assurance partners by capability depth and region. No partner pays for placement on this directory.
AI red team engagements typically split into four workstreams. Application-level red teaming of deployed LLM applications, covering prompt injection (direct and indirect), jailbreaks, output handling vulnerabilities, and the OWASP LLM Top 10. Agentic system red teaming, the fastest-growing area, where adversaries chain tool calls and exploit trust boundaries between an agent and connected systems. Model-level evaluation, covering safety, bias, content policy compliance, and capability uplift testing for frontier models. Programme assurance and audit, where partners produce evidence and findings aligned with NIST AI RMF, ISO/IEC 42001, and the EU AI Act for regulated buyers.
Three procurement archetypes recur. Specialist AI security boutiques (Trail of Bits, Bishop Fox, Haize Labs, Lakera, HiddenLayer, well-developed Intelligence) hold the deepest adversarial-research benches and typically deliver the most original findings; they cost more per engagement but find more. Big Four and global SIs (Accenture, Deloitte, KPMG, IBM X-Force, Mandiant) lead where AI red teaming sits inside a wider AI governance or audit programme; their advantage is integration with existing assurance functions, not depth of original technique. India-heritage SIs compete on multi-year managed AI assurance at lower day rates with slower ramps. Friction point: many engagements still mistake one-shot prompt fuzzing for red teaming. Genuine red teaming requires adversarial creativity, multi-step attack chains against deployed agents, and findings that survive a mitigation cycle.
For complementary research see LLM guardrails, AI security platforms, LLM evaluation tools, and AI governance platforms. For adjacent services see LLM evaluation services, AI governance consulting, generative AI implementation, cybersecurity services, MDR services, and prompt engineering services.
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