LLM Security & Red-Team Testing

The buyer problem

Teams shipping LLM-powered features (chatbots, agents, retrieval-augmented applications) face a genuinely new attack surface: prompt injection, jailbreaking, data exfiltration via crafted inputs, and adversarial manipulation of model outputs. Traditional application security testing does not cover these failure modes, and the vendor landscape is young enough that buyers often can't tell a genuinely AI-native red-team firm from a traditional pentest shop that added 'AI' to its service list. The practical risk is retaining a vendor whose team has never actually tested an LLM application in production, discovered only after a real incident.

What a llm security & red-team testing engagement covers

LLM security and red-team testing vendors run structured, adversarial testing against deployed or pre-deployment LLM applications and AI agents. This typically includes automated and manual prompt-injection testing, jailbreak resistance testing, data-leakage testing (can the model be manipulated into revealing training data, system prompts, or other users' data), and increasingly, testing of autonomous agent behavior for unintended actions. Output is a findings report mapped to a recognized taxonomy (most commonly the OWASP LLM Top 10) with severity ratings and remediation guidance, not a generic penetration-test template with AI terms substituted in.

Methods and techniques

• Automated adversarial prompt testing: running large libraries of known and generated adversarial prompts against the target system to find prompt-injection and jailbreak vulnerabilities
• Manual red-teaming: security researchers manually probing for novel attack chains an automated scanner would miss, including multi-turn conversation attacks
• Data-leakage and exfiltration testing: attempting to extract system prompts, training data artifacts, or other users' context through crafted inputs
• Agent-specific testing: for autonomous or tool-using AI agents, testing whether the agent can be manipulated into taking unintended actions (e.g. unauthorized API calls, data access outside its intended scope)
• Supply-chain and model-integrity checks: scanning third-party or fine-tuned models for embedded malicious behavior or tampering before deployment

What to verify before you retain

• AI-native testing history. Ask for the firm's track record specifically testing LLM/AI systems, not general application penetration testing with AI added to the marketing copy. Request a redacted sample report if possible.
• Taxonomy alignment. Confirm findings are mapped to a recognized taxonomy such as the OWASP LLM Top 10, which makes severity and remediation guidance comparable across engagements and vendors.
• Scope: pre-deployment vs. production. Clarify whether testing happens before launch, continuously in production, or both - these require different engagement models and different levels of access to your system.
• Agent-specific capability. If you're deploying autonomous or tool-using agents (not just a chatbot), confirm the vendor has specific experience testing agent behavior, not just conversational prompt injection.

Questions to put in your RFP

1. How many LLM/AI-specific red-team engagements has your team completed, and can you share a redacted sample finding?
2. What taxonomy do you map findings to (e.g. OWASP LLM Top 10)?
3. Do you test pre-deployment, continuously in production, or both? What does each engagement model cost and include?
4. Do you have specific experience testing autonomous or tool-using AI agents, not just conversational chatbots?
5. What access to our system do you need, and how do you handle any sensitive data encountered during testing?
6. What does your remediation-verification process look like after we fix a finding?

Red flags

• A firm that cannot name a single AI-specific taxonomy (like OWASP LLM Top 10) it tests against.
• Marketing language claiming testing 'eliminates' or 'guarantees' protection against prompt injection - no vendor can make this claim; adversarial AI security is an ongoing arms race, not a one-time fix.
• No distinguishable difference between the firm's AI security offering and its generic penetration-testing service line.
• Reluctance to share any redacted sample findings report or explain its finding-severity methodology.