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Comparison

LyraShield AI vs RunSybil

How LyraShield AI compares to RunSybil for AI black-box pentest. Evidence model, verification approach, coverage framework and deployment model differences.

Last reviewed:

Factual comparison. RunSybil is an AI-native offensive security platform whose "Sybil" agents reason like elite attackers — black-box first, mapping the attack surface, chaining vulnerabilities across code, APIs, cloud and infrastructure and validating exploitability continuously on every deployment. LyraShield AI is release assurance for AI-built apps: a target → review → evidence → fix → retest → report loop with evidence states, checksum-bound assurance reports and reviewed fix proposals. RunSybil validates findings by live exploitation. LyraShield records detected findings and confirms fixes with a deterministic retest. Neither replaces the other.

Where we differ

  • A launch verdict in three states: ready, not ready or insufficient evidence.
  • Every finding keeps its evidence state, and a fresh retest confirms a fix.
  • Fix proposals wait for a human approval before any pull request opens.

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Core approach

Aspect LyraShield AI RunSybil
Primary focus Release assurance for AI-built apps AI-native black-box offensive testing that automates hacker intuition
Scanning approach Agentic engine with coverage framework and evidence states; AI-pattern focus Hierarchy of reasoning agents: map surface → hypothesis-driven tests → oversee campaign; black-box first, white-box-capable
Finding lifecycle Detected → retest-confirmed or inconclusive Hypothesis → confirmed/reproducible finding → prioritized → AI-ready remediation guidance
Control framework Vibe Security 50 (43 code/URL review + 7 evidence-required) No published control framework found; CTEM Phase 4 (Validation) focus
Access model App-layer + source/MCP/agent configs Black-box first (no source required); accepts white-box context
Fix model Recorded fix proposals; Fix PRs open only after a human approves, with a server-generated patch AI-ready remediation guidance integrated with coding tools; PR-level feedback

Capability comparison

Capability LyraShield AI RunSybil
Agentic / AI-driven pentest Yes (app-layer) Yes (hierarchical multi-agent)
Independent finding verification No. Fixes are confirmed by deterministic retest; independent verification is a reserved state Yes (live exploitation; reproducible findings)
Black-box (no source required) Source/MCP-aware (not black-box-first) Yes (core differentiator)
SCA (dependency scanning) Yes (engine) Not a primary focus
Secret scanning Yes (engine + GitHub Action) Not a primary focus
Evidence states (DETECTED / VALIDATED / INCONCLUSIVE) Yes; VERIFIED is future-only and not produced today Findings are confirmed/reproducible; no explicit multi-state lifecycle
Deterministic retest Yes Continuous re-evaluation on every deployment
Coverage receipts Yes (per-control) No
Assurance reports (checksum-bound) Yes Pre-validated findings with reproducible evidence
MCP server integration Yes (runs checks and records evidence inside AI coding agents) Yes (RunSybil MCP server for findings and remediation; cannot launch tests via MCP)
Permission-gated Fix PR requests Fix PR requests require permission and a server-generated patch No (remediation guidance + PR feedback, not permission-gated Fix PR requests)
Multi-tenant / business-logic testing App-layer Yes (cross-tenant access, privilege escalation, transaction manipulation)
AI-generated-code focus Built for AI-built apps Not specific to AI-generated code

Deployment and pricing

Aspect LyraShield AI RunSybil
Deployment Hosted + CLI + MCP + GitHub Action Hosted; point at a target or run continuously; PR-level feedback
Pricing See pricing for current plan details Check the vendor’s current pricing or sales quote
Compliance posture Assurance-record orientation for release sign-off Used for SOC 2 pentest requirements; CTEM Phase 4 validation

When to use which

Use LyraShield AI when

  • Your app is AI-built and you need AI-specific pattern coverage and a release-gate assurance record
  • You need checksum-bound assurance reports with coverage receipts for compliance or client handoff
  • You want reviewable fix proposals and a separate permission gate for Fix PR requests
  • You want security checks inside your AI coding agent via MCP
  • You need SCA + secrets + agentic pentest in one release-assurance loop

Use RunSybil when

  • You want black-box-first offensive testing that reasons like an attacker without needing source code
  • Your priority is continuous, hypothesis-driven exploit validation across code, APIs, cloud and infrastructure
  • You need multi-tenant and business-logic testing (cross-tenant access, privilege escalation, transaction manipulation)
  • You want PR-level security feedback on every deployment, replacing point-in-time pentests and bug bounties
  • You want a CTEM Phase 4 (Validation) engine that proves what your other tools found is actually exploitable

RunSybil automates attacker intuition black-box; LyraShield AI gates AI-built-app releases. Read our comparison methodology and try the free browser-local tools at lyrashieldai.com.

Where RunSybil is genuinely strong

Its core value is black-box, no-source-code offensive testing that reasons like an attacker. Its blog on automating hacker intuition says Sybil operates outside-in, crawling the application to map the attack surface, developing hypotheses about function and purpose and testing them continuously. Its home page says it maps the stack, chains vulnerabilities across layers and re-evaluates on every deployment.

It is multi-agent and deployment aware. An independent review on AppSecSanta describes a Discovery Agent that maps the surface, an Attack Agent that validates findings and login agents for authenticated exploration, with the agents learning about the target as they go.

RunSybil positions itself apart from code review assistants because it interacts with the running system rather than reading source. It chains an application flaw into an infrastructure entry point, which is ground a single-layer scanner misses. For a team that wants continuous black-box offensive testing across a full stack, RunSybil is a credible choice.

Sources

Methodology and scope

This comparison describes published capabilities, not an independent test of either product. Client workflow availability and commercial terms can change. Read how LyraShield tests, records evidence and reports coverage for its assurance model and verify vendor details before a purchasing decision.

LyraShield pricing

Trial: 60 one-time agent-minutes; Starter $29/month; Pro $99/month; Agency $499/month; Enterprise from $1,500/month.

View pricing and plan limits

Frequently asked questions

Does LyraShield replace RunSybil?
No. RunSybil is an AI-native black-box offensive platform whose Sybil agents reason like elite attackers without requiring source code, testing multi-tenant and business-logic flaws continuously on every deployment. LyraShield in open beta is source and MCP-aware release assurance for AI-built apps with SCA, secrets, evidence states and reviewed fix proposals.
Can I use RunSybil and LyraShield together?
Yes. Use RunSybil as the black-box validation layer that attempts attacker-style checks, then use LyraShield to record its own target scope, coverage receipts, evidence states and retest outcomes. RunSybil serves as CTEM Phase 4 validation; LyraShield provides a scoped record for release review.
When should I choose RunSybil over LyraShield?
Choose RunSybil when you want hypothesis-driven offensive testing without handing over source, with cross-tenant access, privilege escalation and transaction manipulation coverage and PR-level feedback. Its black-box-first model genuinely mimics attacker intuition. Choose LyraShield when you need inside-the-agent checks via MCP and reviewed fix proposals.

What happens next

Run the free Lite Check on your own app, then start a trial to review the repository behind it.