AI red teaming vs penetration testing
AI penetration testing asks whether an AI system can be technically exploited. AI red teaming asks whether it can be manipulated into unsafe or unintended behaviour. Bishop Fox, which sells both, draws exactly that line and recommends doing both.
The overlap is real: prompt injection that leaks a system prompt is both a security exploit and an unsafe behaviour. The difference is in the goal, the scope and the deliverable.
Side by side
| Dimension | AI penetration testing | AI red teaming |
|---|---|---|
| Goal | Find technically exploitable weaknesses: in the app, its integrations, data access and infrastructure. | Find ways to make the system behave unsafely: harmful output, policy bypass, tool misuse, data leakage through conversation. |
| Roots | Traditional application and infrastructure security testing. | Security red teaming, extended (per Microsoft) "beyond traditional cybersecurity" to cover harmful outputs. |
| Pass/fail? | Exploitable or not; vendors here map findings to OWASP lists (Bishop Fox names the LLM, ML and Agentic Top 10s). | Open-ended. Findings are behaviours; Pillar Security says its include transcripts and reproduction steps. Microsoft says red teaming is "not safety benchmarking". |
| Who does it | Security testers (consultancies and crowdsourced platforms in this comparison). | Security testers, software, or both; Microsoft recommends a diverse team with adversarial and benign-user mindsets. |
| Cadence | A scoped engagement; Bugcrowd's AI Pen Test includes a final report and one retest. | Iterative; Microsoft recommends testing with and without mitigations, and several platforms run continuously or CI/CD-triggered. |
| Example findings | Excessive agency over a tool, SSRF through a plugin, insecure output handling. | Multi-turn jailbreak, indirect prompt injection via a retrieved document, biased or toxic responses. |
Why the line blurs for LLM apps
Microsoft notes that "red teaming" historically meant systematic adversarial attacks for testing security vulnerabilities, and that with LLMs the term "extended beyond traditional cybersecurity" to probing for harmful outputs. The OWASP Top 10 for LLM Applications (2025) mixes classic security items (LLM03 Supply Chain, LLM05 Improper Output Handling) with behavioural ones (LLM09 Misinformation). A vendor labelled "pen test" and one labelled "red team" may run the same prompt injection attack and file it under different headings.
Which do you need?
- You are shipping an LLM feature into an existing product. You need both. The pen test covers the integration surface (tools, data stores, auth); the red team covers what the model will say and do when pushed.
- You are a model provider. Red teaming is the core requirement. In the EU, Article 55 of the AI Act requires providers of general-purpose AI models with systemic risk to conduct and document adversarial testing. See EU AI Act and red teaming.
- You need compliance evidence. Choose a vendor that maps findings to the frameworks your auditors expect. The comparison table shows who claims OWASP, MITRE ATLAS, NIST AI RMF and EU AI Act mappings.
- You want repeatable checks on every release. That is what the automated platforms sell, often with a CI/CD trigger. A human engagement still makes sense before launch.
Who sells what
From the comparison; alphabetical, not ranked.
Managed service (5)
Scoped human assessments. The consultancies and crowdsourced providers here: Bishop Fox, Bugcrowd, HackerOne, NCC Group, Trail of Bits. Bishop Fox explicitly offers both an AI pen test and an AI red team engagement.
Both (platform + service) (4)
Adversa AI, Gray Swan, Lakera, Mindgard. Mindgard's managed engagements typically run 4-6 weeks per application alongside its platform.
Automated platform (14)
Cisco AI Defense, Enkrypt AI, F5 AI Red Team, Giskard, HiddenLayer, Holistic AI, Lasso Security, Noma Security, Pillar Security, Prisma AIRS (Palo Alto), Promptfoo, Repello AI, SPLX, Straiker.
Timelines you can expect
Published figures: HackerOne runs 15- or 30-day AI red teaming engagements that launch in about a week. Mindgard's managed service typically takes 4-6 weeks per application. Bugcrowd's AI Pen Test can start within 72 hours and includes a final report and one retest. Pricing for all three is by quote; see what AI red teaming costs.
Sources
- Bishop Fox: AI/LLM security assessment (pen test vs red team): https://bishopfox.com/services/penetration-testing-services/ai-llm-security-assessment
- Microsoft Learn: Planning red teaming for LLMs: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/red-teaming
- Microsoft AI Red Team lessons (arXiv 2501.07238): https://arxiv.org/abs/2501.07238
- NIST AI 600-1 (PDF): https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
- OWASP Top 10 for LLM Applications (2025): https://genai.owasp.org/llm-top-10/