Red Teaming

Red teaming applies an attacker's perspective to an authorized assessment of a system. In generative AI applications, the objective is to discover whether an attacker can manipulate model behavior or exploit surrounding components to violate a defined security or safety requirement. Examples include extracting another tenant's documents, persuading an assistant to generate prohibited content, or causing an agent to invoke a privileged tool. Unlike a routine evaluation of answer quality, a red-team exercise deliberately searches for ways around existing defenses.

Start by defining the threat model, rules of engagement, and measurable failure conditions. Identify attacker-controlled surfaces such as chat messages, uploaded files, retrieved documents, external web pages, and tool responses. Establish which data and actions must remain protected. Use isolated cloud accounts or environments, synthetic secrets, restricted tool permissions, request budgets, and explicit authorization so testing cannot unintentionally expose production data or trigger real transactions.

Test both direct and indirect prompt injection. A direct attack might instruct an assistant to ignore its application rules and reveal confidential context. An indirect attack might place instructions inside a retrieved document that tell the assistant to transmit information through an available tool. Jailbreak attempts target behavioral restrictions through techniques such as role-play, obfuscation, multilingual requests, or multi-turn persuasion. These categories overlap, but detecting harmful content is not the same as detecting an attempt to override instructions.

Assess layered input and output controls rather than relying on a single filter. Input validation should enforce expected types, sizes, and schemas, while sanitization and normalization should handle application-specific encodings and unsafe markup. Prompt-injection detectors and safety classifiers can flag suspicious requests, but neither keyword filtering nor text sanitization reliably removes malicious meaning from natural language. Treat retrieved content as untrusted data, preserve instruction boundaries, and test whether those boundaries hold under attack.

Output controls should check for sensitive information, prohibited content, and unsafe formatting before a response reaches a user or downstream system. Validate proposed tool calls against schemas and enforce authorization in application code using the authenticated user's permissions. For example, a support assistant must not issue a refund merely because a model generated a syntactically valid tool request. High-impact actions may require explicit human approval. A safe final answer also does not prove safety if an unauthorized tool action already occurred.

Automated adversarial workflows can generate or mutate test cases, execute conversations, capture detector decisions and tool activity, and compare outcomes with expected policies. Keep successful attacks as regression tests and rerun them when models, system prompts, retrieval sources, tools, or guardrail settings change. Include multi-turn scenarios and benign requests that resemble attacks. Measure attack success, unauthorized actions, false positives, latency, and cost; distinguish attacks blocked by a detector from attacks that failed for unrelated reasons.

Automation provides breadth and repeatability, while human testers contribute contextual reasoning and novel attack strategies. Model-based judges can help triage results but can misclassify outcomes, so consequential findings need evidence and review. Aggressive filtering may reduce attack success while unnecessarily blocking legitimate work. Red teaming is therefore an iterative process of discovering failures, implementing controls, and retesting—not a certification that an application is secure or a substitute for least privilege, tenant isolation, and secure software engineering.