Compare
AI agent security testing, compared
Several kinds of testing get called AI security testing. These pages compare them using public definitions from sources such as NIST and OWASP: what each covers, what it produces and when it is the better choice. Each page also says honestly when another option fits better than an Agent Action Pentest.
Pages in this section
- AI red teaming vs an agent action pentestBroad AI red teaming compared with a fixed-scope test of agent actions.
How these pages work
Buyers often meet three or four labels for overlapping work: AI red teaming, LLM pentesting, automated scanners, traditional application pentests. They differ in scope, target, output and timing, and the right choice depends on whether you are worried about what a model says or what an agent does.
Our comparison pages stick to definitions that can be cited, such as the NIST glossary entry for a red team and the OWASP GenAI Red Teaming Guide. We do not quote competitor prices or make claims about other providers that we cannot source. Each page includes a table of differences and a section on when the other option is the better fit.
The first comparison covers AI red teaming against an agent action pentest. Planned next: LLM pentest versus a traditional pentest, and automated LLM scanners versus a manual agent pentest.
A useful rule of thumb while you read: if the harm you fear would show up in a transcript, you want broad model testing. If it would show up in a bank statement, a deleted table or another customer's inbox, you want the agent's actions tested.
Each comparison also links back to the risk pages, so you can check whether the risk you care about is one an agent pentest exercises. Prompt injection and excessive agency are the two that most often decide the question, because they describe how an outside instruction becomes a real action.
We will also say when a comparison does not favor us. A fixed-scope agent test is a poor fit for a model you are still training, a chatbot with no tools, or a program that needs months of continuous coverage. Those cases are named on each page so you can rule us out quickly.
If you are still deciding what kind of test you need, a 15-minute call is the fastest way to find out whether your risk sits in the agent's actions. If it does not, we will say so. The offer and booking link are below.
Agent Action Pentest: $2,500 fixed. Written report within 72 hours of access, or a full refund.
See the offer or email yaz@plaw.io
Questions
Do comparison pages mention other vendors?
No. They compare categories of testing using public definitions, not named providers or their prices.
Is an agent action pentest a replacement for an application pentest?
No. It targets the agent and its tools. Your web app, APIs and infrastructure still need their own testing.
Which comparison should I read first?
If you have heard the term AI red teaming from a vendor or auditor, start with that page; it is the most common source of confusion.
Related pages
- AI agent security risksAI agent security risks with OWASP and MITRE ATLAS references: prompt injection, excessive agency, data disclosure and MCP tool poisoning, and how we test.
- AI agent security standards, mappedOWASP Top 10 for LLM Applications, OWASP Agentic AI threats and MITRE ATLAS, each mapped to what an Agent Action Pentest tests and what it leaves out of scope.
- AI agent framework securityHow AI agent frameworks like the OpenAI Agents SDK and LangChain expose tools, handoffs and guardrails, which risks apply, and what an agent pentest checks.
- AI agent integration securityWhat AI agents can do through Stripe, Slack, GitHub and Gmail: actions, abuse paths, least-privilege notes from vendor docs, and how we test each.
Sources
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