How do I red-team and test an LLM application for prompt injection and jailbreaks before launch?

To red-team and test an LLM application for prompt injection and jailbreaks before launch, implement continuous, automated adversarial testing using specialized frameworks and a comprehensive golden dataset. This addresses OWASP LLM01 Prompt Injection and NIST-MEASURE-2.7.

• Automated Red-Teaming Tools: Utilize automated red-teaming tools like LAAF, Garak, or PyRIT to generate adversarial inputs at scale and run them against every release candidate. LAAF, for instance, is designed to exploit Logic-layer Prompt Control Injection (LPCI) vulnerabilities in agentic LLM systems, which differ from standard prompt injection by exploiting external system architecture like persistent memory and RAG pipelines. LAAF uses a 49-technique taxonomy to generate over 2.8 million unique payloads and has achieved an 84% aggregate breakthrough rate in empirical evaluations.
• Golden Dataset: Establish a curated "golden dataset" of inputs that covers the security and safety surface, including known prompt injection variants, jailbreak attempts, and edge cases. This dataset should be continuously updated with outputs from automated red-teaming tools and used to test every change to prompts, models, tools, or policies, with regressions blocking merges.
• Multi-dimensional Evaluation: Employ a robust evaluation harness to measure various metrics beyond just safety scores, such as task success rates, refusal rates, tool selection quality, cost per task, latency, and consistency. This prevents shipping changes that might improve one metric but degrade overall performance or security.
• Address LPCI Vulnerabilities: Be aware that static defense filters are often insufficient against sophisticated attacks like LPCI, which can use encoding and conditional activation to bypass plaintext content filters. Implement runtime logic validation alongside standard output filtering, as semantic reframing techniques have proven effective in bypassing defenses.
• Continuous Evaluation: Recognize that red-teaming is not a one-time event but an ongoing process. Integrate security evaluation into the CI/CD pipeline, and consider production evaluation methods like shadow-mode evaluation and canary deployments to monitor for anomalies with live traffic.
• Guardrail Implementation: Implement guardrails that inspect LLM traffic, prompts, responses, and tool calls at runtime to detect prompt injection and sensitive-pattern risks before they propagate. This allows for severity-based allow/warn/block actions and provides auditable records.