Comparison
LyraShield AI vs Corgea
How LyraShield AI compares to Corgea for LLM-core SAST and auto-fix. Evidence states, retest workflows, coverage framework, and approval-gated fix differences.
Last reviewed:
Factual comparison. Corgea is an AI-native application security platform spanning AI SAST, SCA, secrets detection, IaC, container scanning, and a multi-agent AI pentest, delivering review-ready fix PRs. LyraShield AI is release assurance for AI-built apps — it separates detection from proof, produces immutable evidence reports, and gates fixes behind approvals. The two overlap on pentest, SCA, and secrets; neither fully replaces the other.
Core approach
| Aspect | LyraShield AI | Corgea |
|---|---|---|
| Primary focus | Evidence-backed release assurance for AI-built apps | AI-native AppSec platform (SAST, SCA, secrets, IaC, containers) plus AI pentest |
| Scanning approach | Agentic engine, coverage framework, evidence states | LLMs combined with static analysis; business-logic detection (“BLAST”) |
| Finding lifecycle | Detected → independently verified → retest-confirmed or inconclusive | Found → reachability prioritized → fix PR opened; pentest auto-retest loop |
| Control framework | Vibe Security 50 (43 code/URL review + 7 evidence-required) | No published control framework; endpoint-aware reachability |
| Fix model | Approval-gated; PR execution blocked until a server-generated patch is bound to exact approval | Fix PRs opened into normal developer review; fixes statically validated before PR |
| AI-generated code focus | Built for AI-built apps; scans agent rules, MCP configs, AI patterns | Security design reviews of PRD/architecture docs before code is written |
Capability comparison
| Capability | LyraShield AI | Corgea |
|---|---|---|
| Static analysis (SAST) | Via engine | Yes (BLAST — business logic, auth, injection) |
| Custom rules | Not a primary feature | Custom and blocking rules (Scale tier) |
| SCA (dependency scanning) | Yes | Yes (reachability-aware, dead-package analysis) |
| Secret scanning | Yes (engine + GitHub Action) | Yes (detection; no shipped rotation feature) |
| Agentic pentest | Yes (core) | Yes (multi-agent, ~4–8 hr, exploitability validation, auditor-ready reports) |
| Evidence states | Yes (4 states) | No explicit evidence-state model |
| Deterministic retest | Yes | Pentest continuous retesting loop |
| Coverage receipts | Yes (per-control) | No |
| Assurance reports | Yes (immutable snapshots) | Auditor-ready pentest reports (SOC 2 / ISO 27001) |
| Approval-gated fixes | Yes (server-generated patch bound to approval) | Standard PR review; no formal approval gate |
| MCP server integration | Yes (23+ agents) | Agent integrations for AI coding tools |
| Security design review | Not a primary feature | Yes (pre-code PRD/architecture review) |
| IDE integration | Via MCP | VS Code, Cursor, Visual Studio, IntelliJ |
Deployment and pricing
| Aspect | LyraShield AI | Corgea |
|---|---|---|
| Deployment | Hosted + CLI + MCP + GitHub Action | SaaS, GitHub App, GitLab/Azure DevOps/Bitbucket/Harness, IDE plugins, CLI, GitHub Action |
| Pricing | Open beta; pricing announced as it matures | Free ($0, ≤2 members, 10 repos); Growth $39/dev/mo; Scale $49/dev/mo; Enterprise custom. AI Pentest: Standard $4,000, Comprehensive $8,000, Enterprise custom |
| Languages | Language-agnostic | 20+ (JS, Ruby, C++, Python, C, PHP, Java, Go, C#, TypeScript, and more) |
When to use which
Use LyraShield AI when
- You need evidence-backed assurance with immutable reports for release decisions
- Your app is AI-built and you want security checks inside your AI coding agent via MCP
- You want fixes gated behind explicit approval before a patch is applied
- You need coverage receipts mapping to a control framework
- You want detection and proof separated so a finding is never conflated with a verified exploit
Use Corgea when
- You want one platform spanning SAST, SCA, secrets, IaC, containers, and AI pentest
- You need business-logic and auth-flaw detection with review-ready fix PRs
- You want pre-code security design reviews of PRD/architecture documents
- You need a fast, compliance-oriented AI pentest with SOC 2 / ISO 27001 evidence
- You want published, per-developer pricing with a free tier
See the evidence approach in action.
Read the methodology or try the free browser-local tools at lyrashieldai.com.
Sources: Corgea homepage, Corgea pricing, Corgea AI SAST, Corgea AI pentest, Corgea secrets scanning, Corgea dependency scanning, Corgea docs, Corgea on GitHub Marketplace.
Methodology and scope
Every claim on this page is drawn from publicly documented capabilities at the date above. Read how LyraShield tests, records evidence, and reports coverage for the assurance model behind the comparison, and treat anything not stated there as out of scope.
For the long-form version of this comparison, including the evidence model and where each tool fits a release gate, read LyraShield AI vs Corgea.
Frequently asked questions
- Does LyraShield replace Corgea?
- No. Corgea is an AI-native AppSec platform spanning AI SAST with BLAST for business-logic detection, SCA with reachability and dead-package analysis, secrets, IaC, containers, plus pre-code PRD security design reviews and a multi-agent AI pentest from $4,000. LyraShield in open beta is a focused release assurance loop with immutable evidence and approval-gated fixes.
- Can I use Corgea and LyraShield together?
- Yes. Use Corgea for continuous AI SAST, dependency reachability, and fast auditor-ready pentest reports, with free tier for up to two members and 10 repos, Growth at $39 per developer per month. Add LyraShield for the evidence-backed release gate where fixes are blocked until a server-bound approval and retest confirms closure.
- When should I choose Corgea over LyraShield?
- Choose Corgea when you want one vendor for code plus compliance pentests, business-logic testing, and review-ready fix PRs with IDE plugins for VS Code, Cursor, and IntelliJ. Its pre-code design review is a genuine differentiator. Choose LyraShield when you need coverage receipts per control, explicit evidence states, and immutable assurance for AI-built app releases.