Key points
- The checklist provides 222 distinct tests across 20 categories for assessing agentic AI systems.
- It targets infrastructure and cloud access gaps often missed during model-focused testing.
- Ravi Rajput designed the resource using OWASP web security testing principles.
A new free checklist offers security teams a comprehensive method for testing autonomous AI systems. According to Cyber Security News, the resource contains 222 specific tests spread across twenty distinct attack categories. This tool helps organizations evaluate risks that extend well beyond standard prompt injection techniques.
By the numbers
- 222 total tests included in the agentic AI red teaming checklist.
- 20 separate attack categories covering infrastructure, memory, and communications.
- 75 tests rated as Critical severity by the checklist author.
What the figures mean
Security researcher Ravi Rajput structured this spreadsheet around the OWASP Web Security Testing Guide. It is an independent resource, not an official OWASP publication. The checklist addresses a common blind spot where teams focus heavily on manipulating models while ignoring exposed infrastructure.
Testers might overlook an exposed MLflow server or a reachable cloud metadata endpoint. These weaknesses can expose credentials and private records without requiring complex attacks against the model itself. The resource includes objectives, testing steps, suggested tools, and expected results for each entry.
The twenty categories follow four distinct phases. Teams first map the attack surface, covering discovery, orchestration, cloud identity, and model supply chains. This initial phase ensures testers understand what an agent can reach before judging the impact of malicious instructions.
Subsequent phases examine inputs, unsafe output handling, and excessive agency. The final phase covers deployment pipelines, privilege escalation, lateral movement, and data theft. This order aligns with broader industry stress on agent inventories and clear limits on autonomy rather than isolated model assessments.
The checklist assigns severity ratings to guide prioritization. Seventy-five tests receive a Critical rating, 108 are High, 30 are Medium, and nine are Low. These are proposed test severities, not confirmed vulnerabilities in any specific product. They indicate potential risk areas rather than existing flaws in every system.
Highlighted cases include cloud credential theft through server-side request forgery. Other tests examine unsafe Python pickle loading that enables remote code execution. The framework also covers tool combinations that turn permitted reads into unauthorized data transfers, as well as forged messages between agents.
What should you do?
Security teams should review their current testing procedures to ensure they cover infrastructure and cloud access. Verify that all agentic systems have clear limits on autonomy and tool usage. Monitor for exposed services like MLflow servers that could bypass model-level security controls.
Background: Attack surface
An attack surface includes every point where an unauthorized user can try to enter or extract data from your environment. Managing it requires mapping all assets, closing unnecessary ports, and enforcing strict access controls. This process reduces risk by limiting the opportunities attackers have to exploit software flaws or misconfigurations in your network infrastructure.
Read the full guide: Attack Surface Management: 8 Questions Answered for Secure Operations
What to do and how to stay safe: agentic AI
- Map the full attack surface of your agentic systems, including cloud identity and supply chains.
- Test for unsafe output handling and excessive agency in tool integrations.
- Verify that cross-customer data isolation filters are active and effective.
- Monitor for lateral movement and privilege escalation paths between agent components.
Step-by-step guide: Shadow AI Explained: 8 Questions Answered for Secure Operations
General security guidance from the BitShield News newsroom. It is not confirmed advice from the organisations named in this story.
Frequently asked questions
Is this checklist an official OWASP product?
No, it is an independent resource created by security researcher Ravi Rajput that applies OWASP testing guide principles to agentic systems.
Does this checklist confirm vulnerabilities in specific AI products?
No, the severity ratings are proposed tests for assessment purposes, not evidence of confirmed flaws in any particular product.
What types of attacks does the checklist cover?
It covers 20 categories including prompt injection, cloud credential theft, unsafe pickle loading, and forged agent communications.
