We Ran 1,200+ Attacks at 7 AI Security Scanners
Prompt injections, jailbreaks, data-leak baiting, and agentic hijacks — on a controlled testbed, scored the same way every time. Here’s the full methodology and where each tool actually held up.
Four picks, four different jobs
No single scanner wins every category — that’s the point of testing six dimensions instead of one leaderboard number. Here’s the fast answer before the full breakdown.
The methodology, step by step
Every tool ran against the same testbed, the same attack corpus, and the same scoring rubric — in the order below — so the only variable was the scanner itself.
Build a controlled, vulnerable testbed
We stood up an isolated sandbox of small LLM-integrated applications deliberately wired with common weaknesses: a RAG chatbot with an over-permissioned retriever, a customer-support agent with tool-calling access to a mock order database, and a code-assistant plugin with shell access.
- Fully air-gapped from production networks, containerized per app
- Each app seeded with synthetic “sensitive” records to test leakage, never real user data
Assemble the attack corpus
We compiled 1,200+ adversarial prompts and payloads mapped to the OWASP Top 10 for LLM Applications categories, plus agentic-specific abuse cases (excessive tool permissions, chained function-calling exploits).
Define the scoring rubric and weights
Six categories, weighted by how often each shows up in real incident reports rather than evenly split. Full breakdown in the next section.
Run each scanner identically
Same default configuration, same detection thresholds, same corpus, same execution order. No tool was given advance knowledge of the attack set, and each got one configuration pass to match realistic first-week setup.
Score and cross-check results
Two reviewers independently scored each tool’s flagged/missed findings against a known-answer key, then reconciled disagreements before the numbers were finalized.
Re-run for consistency
Each scanner was run three times on the full corpus; scores shown are the average, and any run-to-run variance over 5 points triggered a manual review of that category.
Scoring weights
Full scoring matrix
Every tool, every category, out of 100. Overall is the weighted average using the weights above — sortable in your head by whichever column matters to you.
| # | Tool | Injection | Jailbreak | Leakage | Supply chain | Agentic | CI/CD | Overall |
|---|
Category-by-category breakdown
The overall score hides interesting spread. These six mini-boards show every tool ranked within a single category so you can weight the decision toward what actually breaks in your stack.
The deep dive
Strengths, watch-outs, and who each tool is actually built for — beyond the leaderboard number.
Which one should you actually install?
Start with PromptSentry or SentinelPrompt
Both stay usable without a security team behind them. SentinelPrompt’s CI/CD hooks mean you get a scan on every pull request for free.
AegisLLM for anything agentic
If your app lets the model call tools or hit APIs on a user’s behalf, AegisLLM’s agentic-abuse detection was the strongest we tested at this tier.
GuardRail AI or VectorShield Scan
Pick GuardRail AI if an audit is the driving deadline; pick VectorShield Scan if you want one tool that’s strong everywhere and don’t want to run two.
RedTeamGPT Suite
The rawest adversarial coverage of the set. Pair it with a lighter tool for day-to-day CI, since its reporting layer is the weakest part of the package.
Limitations of this test
What this showdown does not tell you
- Scores reflect one snapshot in time — vendors ship detection updates constantly, and a scanner’s ranking can shift within weeks.
- The testbed uses synthetic apps and synthetic “sensitive” data; your production system’s specific integrations may surface different weak points.
- Default configurations were used throughout — every tool here has tunable thresholds that can move its scores in either direction.
- This is a template report populated with illustrative sample data for structure and formatting purposes, not a live, currently-verified benchmark. Replace the numbers with your own test run before publishing or relying on them.
Further reading
On Code Talent Hub
- AI-Support Tools archiveMore reviews and guides on AI coding and security tooling.
- YouTube API Key AuthenticationA hands-on look at securing API keys and quotas — the same discipline applies to LLM API keys.
- Top open-source tools to boost your workflowWhere SentinelPrompt and ModelWatch-style open-source options fit into a wider dev toolkit.
- Workflow enhancement vs. automationWhy bolting a scanner onto CI isn’t the same as building security into the workflow.
- Dev Workflow Enhancers categoryMore on wiring tools like these into your day-to-day pipeline.
Authoritative external sources
- OWASP Top 10 for LLM Applications (2025)OWASPThe risk taxonomy our attack corpus categories are mapped to.
- MITRE ATLASMITREKnowledge base of real-world adversary tactics against AI systems.
- NIST AI Risk Management FrameworkNISTThe governance framework behind our “supply chain / provenance” scoring category.
- OWASP GenAI Security ProjectOWASPOngoing community research this space moves fast enough to need.