perch vs Mindgard
Side-by-side comparison of features, pricing, and ratings
perch or Mindgard: which should you choose?
| Dimension | perch | Mindgard |
|---|---|---|
| Category | Semantic code linter for repo-defined rules | Automated AI red teaming / runtime AI security platform |
| Pricing | Free; install via npm install -g @lakeday/perch | Contact sales (no self-serve, no published per-seat) |
| Core workflow | perch scan / perch check against your source tree | Discover → Recon → Attack → Defend against models, agents, tools |
| Where rules live | Your own perch.yaml rules written with --ensure plain-language guarantees | Attack library of 150+ publicly disclosed AI vulnerabilities |
| Integrations | GitHub Actions, Claude Code, Codex, Cursor | Burp Suite, GitHub |
| CI/CD gate | Fails CI with exit code 3; perch scan --since origin/main scans only changed files | Shifts AI security into CI/CD via GitHub (best_for: AI eng teams) |
These two do not compete for the same budget, so there is no head-to-head winner. If your problem is adversarial risk in production AI agents — shadow AI, guardrail bypasses, exploit-backed findings your GRC team can audit — Mindgard is built for exactly that, and you will pay a contact-sales enterprise price for it. If your problem is code-level policy enforcement inside a repo (off-by-one loops, MD5-hashed tokens, unhandled nulls, or catching what Claude Code/Codex/Cursor wrote), Perch is free, runs from your terminal or GitHub Actions, and expects you to write your own perch.yaml rules. Pick by problem, not by category: agent attack surface vs source-tree defects.
Semantic code linting: write custom plain-English rules for every pull request and catch AI-written bugs and security gaps.
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Mindgard is AI red teaming that reconnoiters, exploits, and retests vulnerabilities in production AI agents and models.
Visit WebsiteWhat do real users say about perch vs Mindgard?
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
perch
70 mentions across 6 sources · 14% positive — critical (weighted across 6 sources)
Reddit, Hacker News, YouTube, Product Hunt, GitHub, Lemmy
What users praise
- • Free and globally installed via npm as @lakeday/perch — zero cost to try.
- • Natural-language rules in perch.yaml let you encode team conventions as reviewable code.
- • Findings include rule ID, file:line, severity, confidence percentage, and enclosing method.
- • Per-finding confidence scoring avoids binary pass/fail and reduces false-positive fatigue.
What frustrates them
- • Almost no independent user reviews or testimonials exist for the actual linter.
- • Open language-filtering bug means shipped scans may flag irrelevant vulnerability classes.
- • No listed integrations with CI/CD, editors, or Slack — everything is manual CLI wiring.
- • Name collision with Google Perch and a board game makes community support hard to find.
Researched Sep 26, 2026
Mindgard
32 mentions across 3 sources · 60% positive — mixed (weighted across 3 sources)
Hacker News, Bluesky, Lemmy
What users praise
- • Disclosure record covers 150+ named AI vulnerabilities including Grok, Sora, Cursor and Kimi
- • Research is serious enough to prompt a Moonshot internal review and BBC coverage
- • Full-disclosure follow-through on Cursor shows a team that pushes vendors after the report
- • Agent-native recon targets realistic attack paths instead of blanket prompt-injection spam
What frustrates them
- • No public customer reviews or product-experience posts in the entire dataset
- • Community pushed back on two findings as 'not vulnerabilities', weakening trust in severity labels
- • Contact-sales only: no pricing transparency, free tier or self-serve trial is visible
- • Pitching 'managed offensive capability' is a hard sell against free Garak and PyRIT
Researched Nov 8, 2026
Who should pick perch, and who should pick Mindgard?
- Security team defending production AI agentsPick: Mindgard
Continuous recon-first red teaming, runtime response, and psychometric agent profiling target exactly the attack surface these teams own.
- GRC / compliance lead needing AI risk evidencePick: Mindgard
AI risk compliance reporting and guardrail-busting evaluation produce auditable findings, whereas Perch reports code findings only.
- Enterprise mapping shadow AI and AI-BOMPick: Mindgard
AI Discovery & Recon plus infrastructure crawling are purpose-built for enumerating unknown models, agents, and tools across an org.
- Small eng team gating AI-written code in CIPick: perch
Free, GitHub Actions and exit-code-3 gating, plus Claude Code / Codex / Cursor integrations, catch repo-specific defects before merge.
- Solo developer or tight-budget teamPick: perch
Mindgard explicitly is not for buyers needing self-serve pricing or tight budgets; Perch installs via npm and costs nothing.
Frequently Asked Questions
perch vs Mindgard: which should you choose?
These two do not compete for the same budget, so there is no head-to-head winner. If your problem is adversarial risk in production AI agents — shadow AI, guardrail bypasses, exploit-backed findings your GRC team can audit — Mindgard is built for exactly that, and you will pay a contact-sales enterprise price for it. If your problem is code-level policy enforcement inside a repo (off-by-one loops, MD5-hashed tokens, unhandled nulls, or catching what Claude Code/Codex/Cursor wrote), Perch is free, runs from your terminal or GitHub Actions, and expects you to write your own perch.yaml rules. Pick by problem, not by category: agent attack surface vs source-tree defects.
Could one team eventually need both?
Yes, but as separate purchases. A company shipping AI agents might use Perch for code-level guarantees in its repository and Mindgard for adversarial testing and runtime defense of those agents — different owners, different budgets, no overlap in output.
Does Mindgard lint my source code or write rules for my repo?
No. Nothing in the data shows source-tree linting or custom rule authoring; Mindgard's outputs are attack findings, AI-BOMs, guardrail evaluations, and compliance reports. Repository policy enforcement is Perch's job, via perch.yaml.
Can Perch test my AI agent's security posture?
No. Perch scans Python and TypeScript source for defects like off-by-one loops, weak cryptography, and unhandled nulls. It has no recon, attack simulation, or runtime defense capability — that is Mindgard's domain.
How do I evaluate Mindgard's cost before talking to sales?
You cannot from public data — pricing is contact-only with no published per-seat rate and no self-serve checkout. Expect a scoping call; Mindgard's own guidance says instant signup and published pricing are not what it offers.
What does Perch cost once my team scales?
As described, nothing — it is free, findings stay local in .perch, and there is no per-seat tier. The trade-off is no hosted dashboard, no multi-user policy management, and no documented REST API or SDK.
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Where can you read more about perch and Mindgard?
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Analysis generated by our model pipeline from live data: