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LyraShield AIOpen beta

Comparison

LyraShield AI vs XBOW

How LyraShield AI compares to XBOW for autonomous web-app pentest. Evidence states, coverage framework, deterministic retest, and deployment model differences.

Last reviewed:

Factual comparison. [XBOW](https://xbow.com/) by XBOW, Inc. is an autonomous offensive security platform that uses AI agents to continuously pentest applications and APIs, independently proving exploitability with working exploits before a finding reaches your team. [LyraShield AI](https://lyrashieldai.com/) is release assurance for AI-built apps: a target → review → evidence → fix → retest → report loop that separates detection from proof and produces immutable assurance records with approval-gated fixes. Both independently verify findings before surfacing them. Neither replaces the other — they optimize for different deliverables (XBOW: continuous exploit-proof; LyraShield AI: ship-ready assurance for AI-generated code).

Core approach

Aspect LyraShield AI XBOW
Primary focus Release assurance for AI-built apps; immutable assurance reports + approval-gated fixes Continuous, proof-driven autonomous pentesting across the attack surface
Scanning approach Agentic engine with coverage framework and evidence states; scans agent rules, MCP configs, AI patterns AI agents that explore apps/APIs like an attacker, chain vulnerabilities into working attacks
Finding lifecycle Detected → independently verified → retest-confirmed or inconclusive Vulnerability → proven with a working exploit → complete case file with remediation
Control framework Vibe Security 50 (43 code/URL review + 7 evidence-required) No published control framework; governance via SOC 2, ISO 27001, PCI DSS, NIS 2 alignment
Scope of coverage App-layer + SCA + secrets + AI-pattern coverage; purpose-built for AI-built apps Application + API attack surface (expanding to broader attack paths)
Report artifact Immutable assurance snapshot + coverage receipts per control Per-finding case file: chained path, working exploit, decision log, remediation

Capability comparison

Capability LyraShield AI XBOW
Agentic / AI-driven pentest Yes Yes (core capability)
Independent exploit validation Yes (verified state) Yes (proof is the central deliverable)
SCA (dependency scanning) Yes (engine) Not a primary focus
Secret scanning Yes (engine + GitHub Action) Not a primary focus
Evidence states (detected / verified / confirmed / inconclusive) Yes (4 states) Findings carry exploit proof; no explicit multi-state lifecycle
Deterministic retest Yes Re-testing to confirm fixes hold
Coverage receipts Yes (per-control) No (per-finding case files instead)
Assurance reports Yes (immutable snapshots) Board-/auditor-ready reporting per finding
MCP server integration Yes (inside AI coding agents) No (platform-centric)
GitHub Action / SARIF output Yes Not advertised as primary
Approval-gated fix execution Yes (PR blocked until server-generated patch bound to exact approval) No (remediation guidance, not executed fixes)
AI-generated-code focus Built for AI-built apps Not specific to AI-generated code

Deployment and pricing

Aspect LyraShield AI XBOW
Deployment Hosted + CLI + MCP + GitHub Action Hosted SaaS (XBOW Console); available on AWS, Google, Oracle, Microsoft cloud marketplaces
Pricing Open beta; pricing announced as it matures Usage-based, scoped to your environment; no published tier table. XBOW Pentest On-Demand starts at $4,000 (one-time per engagement); ongoing enterprise pricing by quote
Compliance posture Assurance-record orientation for release sign-off SOC 2, ISO 27001, PCI DSS, NIS 2 alignment; auditable scope and logging

When to use which

Use LyraShield AI when

  • Your app is AI-built and you need AI-specific pattern coverage (agent rules, MCP configs, AI-introduced flaws)
  • You need an immutable assurance record and coverage receipts for a release decision or client handoff
  • You want fixes approval-gated so a PR cannot execute until a server-generated patch is bound to an exact approval
  • You want security checks living inside your AI coding agent via MCP, not just in a separate platform
  • You need SCA + secrets + agentic pentest in one release-assurance loop

Use XBOW when

  • You want continuous, autonomous exploit-proof across a broad app + API attack surface, not a release gate
  • Your priority is independent exploit validation with reproducible working exploits and full case files
  • You operate at portfolio scale (many apps) and want headcount-free testing that scales with coverage
  • You want to buy through existing cloud-marketplace commitments (AWS/GCP/Oracle/Azure)
  • You need SOC 2 / ISO 27001 / PCI DSS / NIS 2-aligned, auditable scope and logging

Both tools independently prove findings rather than dumping detections. Read our comparison methodology and try the free browser-local tools at lyrashieldai.com.

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 XBOW.

Frequently asked questions

Does LyraShield replace XBOW?
No. XBOW is an autonomous offensive platform that proves exploitability with working exploits, decision logs, and complete case files at portfolio scale across apps and APIs. LyraShield in open beta is a focused release assurance loop for AI-built apps that separates detection from proof and adds SCA, secrets, and approval-gated fixes with immutable reports.
Can I use XBOW and LyraShield together?
Yes. Teams often run XBOW for continuous autonomous pentesting across a broad estate, and add LyraShield for the per-build release gate. XBOW delivers exploit-proof case files; LyraShield delivers target, review, evidence, fix, retest, report with evidence states. XBOW offers on-demand from $4,000; LyraShield is live in open beta with open registration.
When should I choose XBOW over LyraShield?
Choose XBOW when you need continuous, attacker-style validation across many apps and APIs, reproducible exploits for SOC 2, ISO 27001, PCI DSS, and NIS 2 evidence, and API-driven testing on every merge. Its strength is portfolio-scale proof. Choose LyraShield when the app is AI-built and the bottleneck is a defensible release decision with coverage receipts.
How does reporting differ between XBOW and LyraShield?
XBOW produces per-finding case files with chained paths, working exploits, and full decision logs ready for auditors. LyraShield produces an immutable assurance snapshot that aggregates coverage receipts per control, evidence states for each finding, retest outcomes, and limitations for the release decision. LyraShield's report is built for sign-off.