Honest comparison with the tools developers actually evaluate alongside Inkog. Where Inkog wins, where it loses, and where another tool is genuinely the better fit.
These are the tools you'd consider instead of Inkog if you're trying to ship a secure agent.
Closest direct competitor. Static scanner for agentic workflows with topology visualization and prompt-hardening features. Excellent topology graph, established commercial backing (SplxAI), and ~30× more GitHub stars than Inkog. Detects vulnerabilities and maps to a generic risk taxonomy.
Where Inkog wins: framework breadth (we cover 21 frameworks across code-first and no-code; they cover ~4 Python frameworks), article-level EU AI Act / NIST / ISO 42001 / OWASP mappings (they use a generic bucket taxonomy), MCP server auditing, AGENTS.md governance verification, MLBOM generation, no-code workflow support (n8n, Flowise, Langflow, Dify, Copilot Studio, Agentforce).
Where they win: bigger community, more mature topology visualization, longer track record, established commercial customer base.
Different scan target. Snyk scans your developer environment — ~/.claude, Cursor, Windsurf, Gemini CLI, Codex configs — for installed MCP servers and agent skills. Detects prompt injection in skill definitions, tool poisoning, hardcoded secrets in MCP server manifests.
Where Inkog wins: we scan the agent application code itself (the LangGraph nodes, CrewAI crews, AutoGen workflows you're shipping). Compliance mappings, broader code-vulnerability detection (SQL injection via LLM, recursive tool-calling, RAG over-fetching), SARIF for the GitHub Security tab.
Where they win: Snyk brand and distribution, deeper coverage of the dev-environment ecosystem, explicit "agent skills" tool-shadowing and toxic-flow detection, much larger user base.
Verdict: not actually competitive — Snyk audits your laptop, Inkog audits your repo. Use both.
Tightly-focused OSS scanner for Claude Code configs (.claude/, settings.json, hooks, MCP servers, skills). Detects hardcoded secrets, permission misconfigs, hook injection, prompt-injection vectors. Has an auto-fix mode.
Where Inkog wins: vastly broader scope. AgentShield only audits Claude Code dev environments; Inkog audits the full agent codebase across 21 frameworks. Plus compliance mappings, MLBOM, MCP server auditing, SARIF.
Where they win: tightly focused on Claude Code (smaller scope = simpler UX), built-in --fix auto-remediation, three-agent deep-analysis pipeline.
Conversational security advisor for Claude Code / Lovable / OpenClaw — produces posture snapshots, prioritized remediation plans, and auto-applies fixes via Claude Code or CI.
Where Inkog wins: deterministic scanner with explicit findings and SARIF; Inkog covers any framework (Trent is Claude-Code-shaped). Inkog is a CLI you can drop into existing CI.
Where they win: continuous-loop UX with auto-remediation, deeper integration with Claude Code workflow, conversational design-time advisor (Inkog has none of this).
These tools solve a different part of the agent-security problem. Use Inkog plus one of these, not Inkog instead of them.
| Tool | What it does | Why it's not a replacement for Inkog |
|---|---|---|
| Lakera AI Red Teaming + Lakera Guard | Runtime adversarial probing + runtime guardrails on a deployed agent | Tests a running endpoint; can't see your code |
| Microsoft AI Red Teaming Agent | Cloud-hosted runtime probing of Azure Foundry agents (PyRIT-based) | Azure-only; runtime, not pre-deploy |
| Straiker | Discover + Ascend (red-team) + Defend (runtime guardrails) | Behavioral / runtime; doesn't read source code |
| Crucible Security | OSS adversarial scanner — 1,000+ attack vectors against an agent URL | Black-box runtime; needs a live endpoint |
| NVIDIA Garak | OSS LLM vulnerability scanner — probes for prompt injection, jailbreaks, data leakage | Runtime probing of LLM endpoints; not code analysis |
| Agentic Security (msoedov) | OSS LLM endpoint fuzzer | Runtime probing only |
| Giskard OSS | LLM/agent evaluation — quality, hallucination, robustness, safety tests | Eval framework; doesn't find code-level security bugs |
| Patronus AI | Continuous LLM evaluation — hallucination, factuality, PII, safety | Eval-focused; tests answer quality, not source code |
Inkog does adversarial testing too via inkog red, but the static-code-analysis core is the differentiator.
| Tool | What it does | Why it's a different conversation |
|---|---|---|
| Endor Labs | Reachability-based SCA / "AI SAST" for code that uses AI libraries | Strong on Python imports + CVEs, doesn't understand agent semantics |
| Cisco AI Defense (ex-Robust Intelligence) | Runtime model validation, model firewalling, enterprise platform | Not a developer CLI — different buyer, different scope |
| GuardFive | MCP-server-focused security (currently a landing site, no public product) | Pre-launch; nothing shippable to compare |
These compete on enterprise platform readiness, not on the developer workflow Inkog targets.
Brutally honest:
- Stars / mindshare: 28 stars vs 5,308 (Giskard), 2,284 (Snyk Agent Scan), 1,852 (Agentic Security), 956 (SplxAI). Inkog is genuinely young.
- Runtime defense: Straiker, Lakera, and Cisco have production runtime guardrails. Inkog Red probes; it doesn't block.
- Hallucination / answer-quality eval: Giskard wins outright. Inkog tests for security and compliance, not whether the agent gives correct answers.
- Auto-remediation UX: AgentShield has
--fix; Trent applies fixes via Claude Code. Inkog reports; you remediate. - Detection backend openness: Inkog's CLI is Apache 2.0 but the engine is closed. Giskard and Crucible are fully open-source.
If you need:
- One scanner across many agent frameworks — including no-code workflows (n8n, Flowise, Langflow, Dify, Copilot Studio, Agentforce) and code frameworks (LangChain, LangGraph, CrewAI, AutoGen, AG2, Semantic Kernel, Azure AI Foundry, etc.)
- Article-level regulatory traceability — EU AI Act Article 14 / 15, NIST AI RMF map/measure/manage, ISO 42001, OWASP LLM Top 10 — not just generic risk buckets
- Pre-deploy / shift-left — runs in CI on every PR, fails the build on regressions, posts SARIF to the GitHub Security tab
- MCP server auditing — first OSS tool to scan MCP servers for tool poisoning, privilege escalation, data exfiltration
- AGENTS.md governance verification — checks that your declared controls match what the code actually does
- MLBOM generation — Machine Learning Bill of Materials for supply-chain compliance
…Inkog is the most complete option on the market today.
Inkog is a static, framework-aware security scanner for AI-agent codebases that runs in CI before deploy. The differentiator is a Universal IR ("LLVM for agents") that lets one ruleset detect agent-specific vulnerabilities — recursive tool calls, missing human oversight, prompt injection sinks, RAG over-fetching, MCP tool poisoning — across 21 code and no-code frameworks, with article-level mappings to EU AI Act, NIST AI RMF, ISO 42001, and OWASP LLM Top 10. It is not a runtime firewall (use Lakera or Straiker), not a hallucination evaluator (use Giskard), not a developer-environment auditor (use Snyk Agent Scan or AgentShield). It's the pre-deploy code review for the agent layer. The closest direct alternative is SplxAI Agentic Radar, which is more mature and better-known but covers fewer frameworks and lacks article-level compliance mapping.