New Agentic AI Red Team Checklist Adds 222 Tests Across 20 Attack Categories

A free agentic AI red-team checklist offers 222 tests across 20 attack categories to assess autonomous AI systems beyond prompt injection, covering infrastructure, cloud access, tools, memory, and agent communications.

The checklist targets a common testing gap. Teams may spend days trying to manipulate a model while overlooking an exposed MLflow server, a reachable cloud metadata endpoint, or a missing customer-isolation filter. These weaknesses can expose credentials and private records without requiring a complex attack against the model itself.

Security researcher Ravi Rajput structures the checklist around the OWASP Web Security Testing Guide, which provides a framework for organized security testing.

His spreadsheet applies that approach to agentic systems, with objectives, testing steps, suggested tools, expected results, severity ratings, and evidence requirements for each test. It is an independent resource, not an official OWASP checklist.

Agentic AI Red-Team Checklist Adds 222 Tests

The 20 categories follow four phases. Teams first map the attack surface, covering discovery, orchestration, cloud identity, and model supply chains. They then examine inputs, prompt injection, system prompt leaks, and unsafe output handling before testing tools, excessive agency, memory, agent networks, and Model Context Protocol servers.

The final phase covers deployment pipelines, privilege escalation, lateral movement, persistence, data theft, resource exhaustion, integrity failures, and voice or multimodal inputs. This order helps testers understand what an agent can reach before judging the impact of malicious instructions.

That wider focus aligns with Cybersecuritynews.com’s coverage of OWASP’s agentic AI security report, which stresses agent inventories, clear limits on autonomy, and ongoing oversight rather than isolated model assessments.

The checklist assigns 75 tests a Critical rating, 108 High, 30 Medium, and nine Low. These are proposed test severities, not confirmed vulnerabilities in a particular product or evidence that every assessed system contains those flaws.

Highlighted cases include cloud credential theft through server-side request forgery, unsafe Python pickle loading that enables remote code execution, and tool combinations that turn permitted reads into unauthorized data transfers.

Other tests examine cross-customer document access, forged messages between agents, and delegation that misuses another component’s privileges.

For memory and retrieval systems, one example checks whether removing a tenant identifier filter exposes another customer’s documents. This tests the boundary between customers, rather than simply asking whether the model produces an unsafe answer.

Related research on malicious MCP servers shows why connected tools deserve attention. Manipulated server requests can steer conversations or trigger unauthorized tool actions when safeguards are missing.

A key feature is evidence classification. Reflective results appear in responses, blind results rely on timing or state changes, and out-of-band results use a controlled callback. Under the checklist’s rules, reflective or callback evidence supports confirmation, while blind-only evidence remains probable.

Rajput urges teams to define written scope before testing, mark excluded checks, and record supporting logs or screenshots beside each result. Destructive checks should run only where explicitly authorized, with staging environments used when needed.

The downloadable spreadsheet maps tests to OWASP and MITRE ATLAS frameworks. Its first release avoids fixed CVE references and asks testers to verify current identifiers before reporting. The value is broader coverage with a clear record of what was tested and proven.

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