AI Red Teaming for Every Agent You Ship

AI red teaming is the practice of deliberately attacking an AI agent, chatbot, or voice agent to find where it breaks before a real user or bad actor does. It is also called LLM red teaming or GenAI red teaming. Autonomous red team agents run prompt injection, jailbreak, data exfiltration, and other adversarial scenarios against your agent, then score every response and hand you a letter grade. The OWASP Top 10 for LLM Applications (2026) ranks Prompt Injection as LLM01, the top risk category for LLM-backed applications. It is the first category TestMu AI red teaming tests for.

TestMu AI generates adversarial scenarios from your agent's prompt and documents, then runs them through the agent's real channel: a chat API, a voice endpoint, or a phone number. Specialized testing agents score each turn, and the run returns a readiness verdict. The agent needs no SDK and no code change. The same flow drives standard agent testing, so security and quality checks share one setup.

Nine categories:

• Prompt injection
• Jailbreak
• Data exfiltration
• PII leakage
• Harmful content
• Overreliance
• Hijacking
• Policy violation
• Technical injection

Each category runs as a distinct set of adversarial scenarios against your agent, scored independently and rolled up into an overall grade.

Three: basic, intermediate, and advanced. Basic covers common, low-effort attack patterns. Advanced pushes multi-turn, chained, and obfuscated attacks closer to what a determined adversary would attempt. Run basic in early development and advanced before a production release.

TestMu AI's adversarial scenario suites are aligned to the OWASP Top 10 for LLM Applications. Teams map red team coverage to these frameworks:

• OWASP Top 10 for LLM Applications: Prompt Injection is LLM01 and Sensitive Information Disclosure is LLM02 in the 2026 edition.
• NIST AI Risk Management Framework: its generative AI profile defines AI red-teaming as a structured exercise to find flaws and vulnerabilities in an AI system.
• MITRE ATLAS: a catalog of the adversary tactics used against AI systems.

Run reports can be exported as evidence for those reviews.

Yes. Red team scenarios run from the dashboard or from the Agent Testing CLI, so the same attacks can run in a terminal or a pipeline. The CLI authenticates with environment variables, prints machine-readable JSON, and returns a non-zero exit code when a run fails. See the Agent Testing CLI documentation for the current install command and options.

Every red team report contains:

• A letter grade from A+ through F
• A per-category breakdown across the 9 attack categories
• Per-metric scores, with the confidence level behind each score
• The full transcript of each failing scenario, annotated with the turn that caused the failure and the evidence behind the verdict

Reports can be exported for compliance documentation.

Yes. Run the red team suite from the Agent Testing CLI in GitHub Actions, GitLab CI, Jenkins, or any other pipeline. Gate the build on the exit code: 0 when the run passes, non-zero when it fails. Runs execute on HyperExecute, with secure tunnels available for firewall-restricted agent endpoints. You can also schedule the suite daily, weekly, monthly, or on a cron expression in your own timezone.

Yes. A red team run stress-tests the same 9 quality metrics used in standard chatbot testing and voice agent testing, including bias detection and hallucination detection. They are scored under adversarial pressure as well as on clean, well-formed inputs.

Both. Attacks go to the deployed LLM application, meaning the chatbot, voice agent, or phone agent your customers reach, through its real endpoint or phone number. That covers LLM red teaming risks such as prompt injection and jailbreaks, and AI agent red teaming risks such as hijacking and policy violation. For a worked test plan, read our guide to AI agent red teaming.

A penetration test looks for flaws in infrastructure and application code, such as broken authentication or an exposed API. AI red teaming targets the model's behavior: it tries to talk the agent into ignoring its instructions, leaking data, or producing harmful output. A manual red team exercise is a point-in-time review, while automated AI red teaming reruns the same attacks on every prompt or model change. To see how the open-source and commercial options compare, read our roundup of AI red teaming tools.

TestMu AI is SOC 2 Type II certified, ISO 27001 certified, GDPR and CCPA compliant, NIST 800-53 Rev 5 compliant, and HIPAA compliant. Every red team run produces an exportable, auditable report with scenario transcripts, category scores, and timestamps for compliance documentation.

Teams shipping any customer-facing AI agent, chatbot, or voice agent that handles sensitive data, financial decisions, or regulated conversations. It pairs naturally with agent testing for functional coverage, so security and quality gates run from the same platform before every release. Agents that act are exposed too. An autonomous or multi-agent workflow with tool access can be steered into misusing those tools by text it retrieves, with no user typing the attack. Agent Assurance treats prompt injection, instruction override, and tool misuse as a generated scenario family for that case, and checks tool calls against the surface the agent declares.

AI red teaming is part of TestMu AI Agent Testing, which is quoted to your evaluation volume. You can create a free account to try it, then contact sales for a quote as usage grows. The pricing page shows how plans are structured.