Runeward vs Sinewave Agent Security Scanner
Detailed side-by-side comparison to help you choose the right tool
Runeward
🔴Developersecurity
Governed execution cells for AI agents: declarative profiles provision isolated Docker or Kubernetes sandboxes with deny-by-default egress, policy gates, human approvals, guardrails, and a tamper-evident audit ledger.
Was this helpful?
Starting Price
$0 (self-hosted)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.
Was this helpful?
Starting Price
Free (MIT license)Feature Comparison
Scroll horizontally to compare details.
Runeward - Pros & Cons
Pros
- ✓Apache-2.0 licensed and self-hostable end to end, no vendor lock-in on the control plane
- ✓Every entry surface — REST, dashboard, CLI, MCP — funnels through the same policy pipeline
- ✓Signed hash-chained audit ledger is verifiable independently of Runeward, which matters for compliance
- ✓Native adapters for seven mainstream agent frameworks, so onboarding an existing agent is a small refactor
- ✓Cost guardrails (token, exec, wall-clock, egress) plus retry-loop detection catch runaway agent spend
Cons
- ✗Kubernetes backend brings real operational weight: CRDs, admission webhook, NetworkPolicy, PSA config
- ✗Policy authoring skill required — teams unfamiliar with CEL or OPA-Rego will face a learning curve
- ✗Pre-1.0 project without published SLAs; no commercial support tier listed on the site
- ✗Effectiveness depends on how tightly your declarative profiles are written; a permissive profile trivially undermines the guarantees
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
Not sure which to pick?
🎯 Take our quiz →Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.
Ready to Choose?
Read the full reviews to make an informed decision