NOTE · 5 SEP 2026
How to run a proof of concept for a coding agent security tool
ANSWER FIRST
Keep the proof of concept small and identical for each tool: the same repository, the same assistants, the same test cases and the same period. Test what the tool does to the agent while it works, not only what it reports at the pull request, and write down what each tool missed as well as what it caught.
Editorial assessment · Compiled from vendor product, docs, pricing and marketplace pages · Rated October 2026
By Best AI Code Security reviewers · Published 5 September 2026
This note turns our scoring criteria into a test plan. It assumes a shortlist of two or three tools, chosen with our buyer's checklist or the side-by-side comparison. Our own scores come from public vendor material, not from running the tools, so a proof of concept is where a buyer checks the claims that matter to them.
1. Set the scope
- One or two repositories where developers already use coding agents, with a normal mix of first-party code and dependencies.
- The assistants your developers actually use. Check that each vendor names them: in our Coverage Census, Cursor is named by 8 of 9 vendors and OpenAI Codex by 3.
- A fixed period, the same for every tool, and one owner who records the results.
- A written list of test cases, agreed before the first install, so that no tool is tested only on what it does well.
2. Test the agent layer
These tests check controls that act on the agent while it works, the part of the stack our scoring weights at 30%. Use harmless test data throughout: a fake key in the format of a real one, a package name you control, and a command that does nothing harmful.
- Inventory. Ask the tool to list the assistants, models and MCP servers in use on the test machines, and compare the list with what you know is installed.
- Secrets in context. Put a test key in a file the agent will read, then ask the agent to work on that file. Record whether the key was blocked before it reached the model, flagged afterwards, or not caught.
- Risky commands. Ask the agent to run a command your policy forbids. Record whether it was blocked, and where: in the IDE, on the endpoint or by a hook.
- Package installs. Ask the agent to add a package your policy should stop, such as a version published in the last day. Record whether the install was blocked or only reported later.
- Guidance before code. Ask for a feature with an obvious security requirement, such as a login endpoint, and check whether the tool changed what the agent wrote.
- Switching it off. Try to disable the control as a developer would. Record what happens and whether the security team is told.
3. Test the code layer
- Open pull requests written by the agent and by a person, each with a known vulnerable pattern, a vulnerable dependency and a test secret.
- Record what each tool reports, where it reports it (IDE, pull request or dashboard), and whether it blocks the merge.
- Check how findings from your existing scanners appear, if the tool pulls them in.
- Count false positives over a normal week of pull requests, not only on the seeded ones.
4. Measure the same things for every tool
| Test | What to record |
|---|---|
| Assistant and MCP inventory | Items found against items known |
| Secret in agent context | Blocked, flagged later or missed |
| Forbidden command | Blocked or not, and where |
| Risky package install | Blocked at install or reported later |
| Seeded pull request findings | Found, blocked or missed |
| False positives | Count per week of normal pull requests |
| Setup effort | Hours from start to first useful result |
| Price for your team | Quote at your committer count, and which plan includes the agent controls |
5. Ask what the results do not show
A short test cannot show how a product behaves across a whole engineering organization. Ask each vendor how policy is rolled out to every developer machine, how exceptions are approved, how the product is updated when a new assistant appears, and what data leaves your environment. Several vendors describe features that only a larger rollout can test properly: Legit Security's anti-tampering in VibeGuard 2.0, Cycode's AI-BOM with commit provenance and Endor Labs' AURI policy at the moment of action are examples.
Where a tool does not act on a particular agent, a hook can fill part of the gap during the test: Claude Code's PreToolUse hook can check a command and block it before it runs. Record which results came from the product and which from your own hooks.
Source: Claude Code hooks documentation · Reviewed Sep 2026
6. Decide
Weight the results the way your team weights the criteria, not the way we do. The calculator lets you set your own weights on our scores; use the same weights on your proof of concept sheet so the two can be compared. Keep the sheet, because the next contract review will need it.
Sources
- Legit Security: VibeGuard 2.0 press release legitsecurity.com
- Cycode: AI security cycode.com
- Endor Labs endorlabs.com
- Claude Code hooks documentation code.claude.com