AI Red Teaming

Deploying generative AI into production environments demands moving beyond general model capability into systematic adversarial stress-testing. This red-teaming assessment evaluates ChatAssist, an automated customer support and account management agent, examining how resilient its safety guardrails remain when subjected to deliberate manipulation. By treating the chatbot not merely as an isolated language model but as a multi-tier orchestration pipeline, the assessment probes key operational failure modes across inbound user inputs, external retrieved context, and downstream transactional execution.

Adversarial testing focused on four primary exploit vectors: direct prompt injection, indirect context contamination, system metaprompt extraction, and safety filter evasion. While standard input filters caught baseline profanity and direct toxicity, targeted multi-turn framing and delimiter hijacking successfully extracted internal system instructions, proprietary refund thresholds, and CRM endpoints. More critically, indirect injection tests demonstrated that embedding hidden directives within untrusted customer order notes could hijack execution logic, exposing the system to unauthorized transaction modifications and policy circumvention.

Rather than treating red teaming as an academic exercise, the assessment translates empirical attack paths into concrete, defense-in-depth engineering controls. Recommendations centre on enforcing strict delimiter boundaries (such as XML encapsulation of untrusted inputs), implementing dual-LLM moderation layers like Llama Guard for real-time validation, and stripping operational secrets from prompts. Crucially, the remediation framework mandates deterministic schema validation and human-in-the-loop gates for high-impact actions, ensuring business-critical logic remains insulated from probabilistic model behaviour.