by CostLens (LX AI)
If you are shipping agents, you need a way to test them before they touch production data. Here is how four tools line up. All four are real; pick by where you are in the lifecycle.
| Tool | Vendor | Type | Primary focus | License |
|---|---|---|---|---|
| CostLens | LX AI | Commercial SaaS | Agent spend attribution, per-agent budget guardrails, and early warnings — FinOps decision-support, not a billing certificate | Proprietary (subscription) |
| AgentRedTeam | LX AI | Commercial SaaS | Adversarial red-team / attack simulation against your agent | Proprietary (subscription) |
| PyRIT | Microsoft | Open source (Python) | Risk identification and red-teaming orchestration for generative AI | MIT |
| Garak | NVIDIA | Open source | LLM vulnerability scanning across known probe categories | Apache-2.0 |
None of these tools give legal advice. The EU AI Act and OWASP mappings are decision-support - confirm obligations against EUR-Lex and your qualified assessor.
Run your own agent through a guardrail pass: Try CostLens
If you are shipping agents, you need a way to test them before they touch production data. Here is how four tools line up. All four are real; pick by where you are in the lifecycle.
Run your own agent through a guardrail pass: Try CostLens
If you are shipping agents, you need a way to test them before they touch production data. Here is how four tools line up. All four are real; pick by where you are in the lifecycle.
Teams evaluating agent security tooling in 2026 usually mix three layers: prompt-injection defenses (OWASP LLM01), tool-allowlist enforcement, and runtime monitoring. A useful comparison grid separates pre-run policy, in-run controls, and post-run evidence. Pre-run policy covers system prompts, allowed tools, and data classification. In-run controls cover sandboxes, rate limits, and human approval gates. Post-run evidence covers logs, red-team transcripts, and incident tickets. If a vendor collapses all three into one score, ask for artifact examples rather than accepting a single percentage.
Category:baseline checklists help more than feature matrices. For each candidate tool, record whether it (1) maps findings to a published framework such as OWASP Top 10 for LLM Applications, (2) supports offline or air-gapped evaluation, (3) exports machine-readable results, and (4) states failure modes honestly. Prefer tools that fail closed when credentials are missing over tools that invent “green” results. That honesty rule matches how production incident reviews actually work.
Shared blog stubs across micro-SaaS fleets often under-serve readers because they stay at definition level. Expand the comparison with decision criteria: latency budget, false-positive tolerance, who owns remediations, and whether the tool can run without uploading proprietary prompts. Write down what you will not automate—destructive tool calls, production write access, and payment mutations should stay behind human approval even when scanners pass.
When you publish a comparison for your own product category, cite primary sources (OWASP, NIST AI RMF) and keep competitor names factual. Do not invent customer win rates. A durable comparison page is a checklist plus worked examples, not a leaderboard.
Repeat the rubric quarterly. Agent frameworks change faster than annual vendor reviews. Keep a short “What this comparison is NOT” note on every page: not a certification, not a penetration test, and not a guarantee that agents will never abuse tools.
Not a pricing guarantee and not a substitute for your finance review.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Document the control owner, the last test date, the fixture path, and the expected fail-closed behavior when credentials or quotas are missing. Record whether results are rule-based or model-assisted, and never present demo output as live AI. Prefer primary standards links over secondary summaries. Keep customer metrics empty until verified. Re-run this checklist after framework updates and after major prompt or tool-registry changes. Store evidence in tickets with timestamps. Reject efficacy language such as absolute guarantees.
Keep a dated evidence folder for each control: fixture input, expected deny or pass behavior, operator notes, and a link to the primary standard you relied on. Re-run after dependency upgrades. If AI assists triage, label the row as model-assisted; if a deterministic rule fired, label it rule-based. Never present demo mode output as live production AI. Prefer short honest summaries over absolute claims. Update this page when framework versions change so RULESET dates stay accurate for reviewers.