AI Red Teaming

Typically, we find that AI red team assessments take around 2 weeks depending on the number of features involved.

You can expect to pay no less than $16,000 for a single AI red team engagement with us, though the scope of your assessment always determines the final price.

AI Red Teams focus on identifying vulnerabilities unique to Generative AI systems—like prompt injection, toxic outputs, model extraction, bias, hallucinations, and alignment failures—by simulating adversarial interactions with AI models such as Large Language Models (LLMs). In contrast, traditional application penetration testing targets infrastructure and software flaws like injection attacks, authentication bypasses, and misconfigurations. While both share foundations like threat modeling, attacker simulation, and risk assessment, AI Red Teaming expands the scope to include the AI model’s behavior, ethical risks, and misuse of generated content. It also requires new evaluation methods due to the non-deterministic nature of AI outputs.

Yes, AI Red Teaming includes traditional penetration testing vectors such as input validation and extends them to cover AI-specific threats. For example, it evaluates how well AI systems validate and sanitize user inputs to prevent prompt injection or adversarial manipulation—much like input validation testing in web applications. Additionally, AI Red Teaming examines improper output handling, which is critical for generative models that produce content. This includes assessing whether the model outputs unescaped HTML or JavaScript that could lead to cross-site scripting (XSS) or code injection in downstream applications. Because AI outputs can be dynamic and context-dependent, Red Teams test how the system handles those outputs across different stages—whether they’re displayed in a web UI, passed to an API, or fed into another model or service. Ensuring both input and output are properly controlled is essential to prevent misuse, leakage, or unintended behavior in production environments.

The number of tokens used can't reasonably be guessed given the nature of the unpredictibilty of models. Additionally, it depends on the size of the model being tested. However, Schellman does not test for token exhaustion attacks unless specifically requested by the client.