Automate Agent Red Teaming

Use Case

Autonomous agents expand the attack surface beyond model responses because they can invoke tools, access data, and execute multi-step workflows. PointGuard AI tests agents and their connected ecosystems so teams can expose weaknesses before those actions become production incidents.

Challenges

The AI environment creates several recurring security and governance challenges:

• Model-only testing misses tool and workflow failures
• Agents can combine safe steps into dangerous outcomes
• Authorization weaknesses emerge during end-to-end execution
• Runtime controls must be validated under adversarial conditions

Solution

PointGuard AI provides a comprehensive solution for testing complete agent workflows, connected tools, and runtime controls under adversarial conditions:

1. Map the agent workflow. Use AI Red Teaming, Agent Mission Control, and MCP Security Gateway to identify the agent, models, tools, integrations, and resources involved so testing reflects the real execution path.

2. Simulate adversarial behavior. Probe agents with prompt injection, jailbreak, unsafe-action, and other attack scenarios designed to expose behavioral weaknesses.

3. Test connected actions. Validate how agents use tools and resources so authorization and workflow failures are detected beyond the model response.

4. Verify runtime controls. Retest guardrails, gateway policies, and containment controls to confirm dangerous behavior is blocked before production impact.

End-to-end agent red teaming helps enterprises validate not only what an agent says, but what it can actually do under attack.

Risks Addressed

Applicable framework risks and controls include:

OWASP Top 10 for LLMs

• LLM01:2026 Prompt Injection
• LLM02:2026 Sensitive Information Disclosure
• LLM03:2026 Excessive Agency
• LLM10:2026 Improper Output Handling

OWASP Top 10 for Agentic Applications

• ASI01: Agent Goal Hijack
• ASI02: Tool Misuse and Exploitation
• ASI03: Identity and Privilege Abuse
• ASI04: Agentic Supply Chain Vulnerabilities
• ASI05: Unexpected Code Execution (RCE)
• ASI06: Memory & Context Poisoning
• ASI07: Insecure Inter-Agent Communication
• ASI08: Cascading Failures
• ASI09: Human-Agent Trust Exploitation
• ASI10: Rogue Agents

NIST AI Risk Management Framework

• MAP 5.1: Likelihood and magnitude of identified impacts are documented
• MEASURE 1.1: Risk measurement approaches and metrics are selected and implemented
• MEASURE 2.7: AI system security and resilience are evaluated and documented
• MANAGE 1.1: Determine whether deployment should proceed
• MANAGE 4.1: Post-deployment monitoring and change management are implemented