Sinewave Agent Security Scanner vs PentAGI
Detailed side-by-side comparison to help you choose the right tool
Sinewave Agent Security Scanner
🔴Developersecurity
An open-source, MIT-licensed security scanner — "npm audit for AI agents and MCP servers" — that audits code, MCP tools, prompts, skills, and AI-suggested dependencies over MCP or CLI.
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Free (MIT license)PentAGI
🔴Developersecurity
Open-source, fully self-hosted autonomous AI penetration-testing platform that runs a team of specialized AI agents with 20+ professional security tools in isolated Docker sandboxes for authorized security testing.
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Sinewave Agent Security Scanner - Pros & Cons
Pros
- ✓MIT-licensed and fully open source — no vendor gating on higher-value features
- ✓MCP-first design lets the coding agent scan its own output without a human in the loop
- ✓Purpose-built for agent-specific failure modes: prompt injection, hallucinated packages, tool spoofing
- ✓Claimed 97.7% benchmark precision, with 120 auto-fix templates to close the loop from detection to remediation
- ✓Lightweight @prooflayer variant is a viable pre-commit or CI check when the full scanner is too heavy
Cons
- ✗Precision claim (97.7%) is self-reported on the vendor's own benchmark, not an independent evaluation
- ✗Full scanner requires Python plus the AST toolchain — heavier install than a pure JS pre-commit hook
- ✗Auto-fixes are template-based; complex vulnerabilities may need human review after the rewrite
- ✗Rule coverage is language-broad but you should still verify depth for your specific stack
- ✗MCP server auditing scores are only as good as the point-in-time snapshot — a compliant server can still rug-pull on a later run
PentAGI - Pros & Cons
Pros
- ✓Fully autonomous AI pentesting in sandboxed, isolated Docker containers. This directly supports the primary workflow.
- ✓Multi-agent team (orchestrator, researcher, developer, executor) with supervision agents.
- ✓20+ professional tools built in (nmap, metasploit, sqlmap, and more).
- ✓Smart memory plus Graphiti/Neo4j knowledge graph and pgvector store.
Cons
- ✗Plan prices can omit usage overages, model-provider charges, implementation work, taxes, or required seats.
- ✗Features and limits can change by plan, billing interval, region, and release; verify current documentation.
- ✗No native MCP support is documented in this record, so agent workflows may require API work or another integration layer.
- ✗A representative pilot is necessary because a feature list does not establish reliability or operational fit.
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