AI red teaming is structured, adversarial testing of AI systems. Using attacker-like techniques to surface failure modes, vulnerabilities, and unsafe behaviors so you can fix them before real-world damage occurs. It combines classic red teaming’s “assume breach” mindset with AI-specific tactics like jailbreaks, prompt injection, data leakage probes, and tool-misuse simulations.  

Unlike generic pen tests, AI red teaming is interactive and iterative across the model/app lifecycle: pre-deployment and post-deployment, probing for toxic, biased, or factually incorrect outputs; secrets and PII leaks; model or prompt extraction; and unsafe agent actions.  

Why red team AI now?

• Expanding attack surface. LLMs add novel failure modes (jailbreaks, indirect prompt injection, over-permissioned tools) beyond traditional IT flaws.  
• Safety & compliance debt. Regulators and customers increasingly expect evidence that you’ve stress-tested for harmful behavior and leaks, before launch and continuously.  
• Shift-left resilience. Continuous, structured adversarial testing lets you harden prompts, policies, and guardrails faster than incident-driven triage ever could.  

How AI red teaming works (at a glance)

1. Scope & threat model: Define assets, user journeys, data sensitivity, tools/permissions, and red-team rules of engagement.  
2. Scenario design: Craft adversarial tasks: jailbreaks, injection chains, data-exfil prompts, tool-abuse playbooks, resource-exhaustion inputs, and social engineering paths.  
3. Execution & logging: Run interactive attacks against models/agents and apps; capture traces, prompts, outputs, and model/tool states.  
4. Scoring & risk: Rate findings by exploitability, business impact, and reproducibility; propose control fixes (prompt/policy/model/app).  
5. Hardening & retest: Patch prompts, add guardrails and filters, right-size tool scopes, add runtime checks; re-run scenarios until risks drop.  

A useful mental model from the field splits AI red teaming into adversarial simulation, adversarial testing, and capabilities testing. Together covering attacker behavior, systematic fuzzing, and boundary-finding for model abilities.  

What you should test

• Jailbreak & safety bypasses (role, instruction, and content constraints).
• Prompt injection (direct/indirect), system prompt leakage, and model/prompt extraction attempts.  
• Sensitive data exposure (PII, secrets, proprietary content) and training-data leakage.  
• Tool/agent misuse (overbroad actions, insecure tool wrappers, confused-deputy attacks).  
• Content harms (toxicity, bias, defamation) and factual failures (hallucinations on high-stakes tasks).  
• Resilience to resource abuse (token bombs, recursion/amplification, DoS-like prompts).  

Red Teaming vs. Governance, Observability, and Supervision

1. Red Teaming
   
   • Proactively find & fix AI vulnerabilities and unsafe behavior
   • Before launch and continuously
   • Attack playbooks, findings, repro prompts, risk scores, fixes
2. Governance
   
   1. Align with policy, ethics, and regulation
   2. Throughout lifecycle
   3. Policies, approvals, evidence packets
3. Observability
   
   1. See behavior in the wild
   2. Post-deployment
   3. Traces, evals, incident reports
4. Supervision
   
   1. Combines all techniques into a comprehensive solution
   2. Pre- and Post-deployment
   3. Guardrails, filters, HITL workflows, red teaming, evals

How Swept AI supports AI Red Teaming

• Scenario library & generators: Start with curated jailbreaks, injection chains, leakage probes, and agent-misuse tasks; extend with your domain specifics. (Informed by open industry patterns.)  
• Campaigns at scale: Run hundreds of adversarial tests across models, prompts, and versions; capture full traces and artifacts for reproducibility.  
• Risk scoring & triage: Auto-score by impact/exploitability; route critical findings to owners with SLAs and retest gates.  
• Fix suggestions: Link each finding to prompt hardening, policy/guardrail changes, tool-permission tightening, or app-level mitigations.  
• Evidence packs: Export audit-ready proof (attacks, outputs, before/after metrics) for stakeholders and compliance reviews.  

KPIs that matter

• Escape rate (successful jailbreaks/total attempts)
• Leakage rate (PII/secrets exposure per N prompts)
• Tool-misuse incidents (unsafe actions prevented vs. attempted)
• Mean time to remediate (MTTR) per critical finding
• Residual risk trend after retest cycles (per app/use case)
  

These metrics show whether your red teaming is lowering exploitable risk versus just generating bug lists.