Strix vs ifixai
Two of the top projects in Security, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Strix
Autonomous AI pentesting agents that validate vulnerabilities with working exploits
ifixai
Python CLI that audits AI agents for policy, access and reliability failures
Verdict
Strix ranks higher, 74 to 51. Of the 3 score parts we measure for both, Strix leads on 2, ifixai on none and 1 is even.
- AdoptionStrix leads by 74 points, 93 to 19: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score ifixai on this; Strix has 77.
- Easy to runWe do not score ifixai on this; Strix has 50.
- Agent-readyStrix leads by 30 points, 30 to 0: a wide gap.
Pick Strix if you are
Developers and security teams pentesting their own apps and CI pipelines
- Validates findings with working PoCs rather than only static-analysis pattern matches
- Targets code, live URLs, GitHub repos and OpenAPI/Postman specs from one CLI
Pick ifixai if you are
Teams testing deployed AI agents for policy and access-control failures
- Tests a real agent over its HTTP endpoint, not only a bare model API
- Default 10-check suite ends in a grade; --full-run covers all 60 inspections
| What we compare | Strix | ifixai |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 93, widely used | 19, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 77, fair | Not scored for this kind |
| Easy to run | 50, easy | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 68.6k | 24.2k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Strix
Strix runs teams of LLM-driven agents against a local codebase, repo URL, web app or API spec, using a Caido HTTP proxy, browser automation, shell and a Python exploit runtime inside a Docker sandbox. Findings come with proof-of-concept exploits and are written to strix_runs/, viewable with a local dashboard via `strix view`. It also runs headless in CI such as GitHub Actions and connects to user-defined MCP servers.
Who it is for: Developers and security teams pentesting their own apps and CI pipelines
Strengths
- Validates findings with working PoCs rather than only static-analysis pattern matches
- Targets code, live URLs, GitHub repos and OpenAPI/Postman specs from one CLI
- Headless mode exits non-zero on findings, with a documented GitHub Actions workflow
- Works with many LLM providers via LiteLLM, including local models through LLM_API_BASE
Weaknesses
- Needs a running Docker daemon and an LLM API key or ChatGPT subscription
- SSO, compliance reports and VPC deployment are Enterprise-only
- One-click autofix and PR reviews are listed under the paid Cloud offering
- Scan quality and cost depend on the chosen model; README gives no cost or hardware figures
- no GPU
- Docker
- Needs Docker, LLM API key, Caido, LiteLLM
- Models: OpenAI, Anthropic, Google Vertex AI, DeepSeek, Moonshot
ifixai
iFixAi runs up to 60 inspections against a model or a deployed agent's HTTP endpoint and grades the answers with a separate judge model. The default run covers 10 checks, such as role-based tool access, prompt injection, privilege escalation and data leakage between users. It writes JSON and Markdown reports plus a terminal scorecard, and also ships as a Claude Code and Codex plugin and a skill for other agents.
Who it is for: Teams testing deployed AI agents for policy and access-control failures
Strengths
- Tests a real agent over its HTTP endpoint, not only a bare model API
- Default 10-check suite ends in a grade; --full-run covers all 60 inspections
- Citable grades use a judge from a different vendor than the system under test
- Runs from CLI, CI flags, a Claude Code/Codex plugin, or a skill for other editors
Weaknesses
- A citable grade needs two API keys from different vendors
- Full-suite judging costs roughly $10-18 per run, plus the agent's own bill
- Bundled default fixture has seeded defects, so a custom fixture is needed for real grades
- Full mode requires a hand-built fixture
- no GPU
- Needs Python 3.10+, LLM provider API keys (system under test and judge), uv (for skill install)
- Models: OpenAI, Anthropic, Gemini, Azure, Bedrock
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