shannon 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.
shannon
Autonomous pentester that exploits web apps and APIs using source code analysis
ifixai
Python CLI that audits AI agents for policy, access and reliability failures
Verdict
shannon ranks higher, 75 to 51. Of the 3 score parts we measure for both, shannon leads on 2, ifixai on none and 1 is even.
- Adoptionshannon leads by 52 points, 71 to 19: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score ifixai on this; shannon has 93.
- Easy to runWe do not score ifixai on this; shannon has 50.
- Agent-readyshannon leads by 55 points, 55 to 0: a wide gap.
Pick shannon if you are
Developers and security teams pentesting apps they own, locally or in CI
- Reports only exploits with a reproducible proof of concept, which cuts speculative scanner noise
- Outputs PDF, Markdown, JSON and SARIF 2.1.0; GitHub Action and GitLab CI can gate on exploited findings
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 | shannon | ifixai |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 71, popular | 19, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | Not scored for this kind |
| Easy to run | 50, easy | Not scored for this kind |
| Agent-ready | 55, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 48.9k | 24.2k |
| License | AGPL-3.0 (copyleft) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | TypeScript | Python |
| Docker | Yes | No |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
shannon
Shannon reads a web application's source, maps attack paths, then uses browser automation and command-line tools to run real exploits against the running app and its APIs. Only findings with a working proof of concept reach the PDF, Markdown, JSON or SARIF 2.1.0 report. It runs from an npx launcher with an ephemeral Docker worker, and also ships a GitHub Action and a GitLab CI component.
Who it is for: Developers and security teams pentesting apps they own, locally or in CI
Strengths
- Reports only exploits with a reproducible proof of concept, which cuts speculative scanner noise
- Outputs PDF, Markdown, JSON and SARIF 2.1.0; GitHub Action and GitLab CI can gate on exploited findings
- Bring your own key: Anthropic, OpenAI, xAI, Bedrock, gateways, or local Ollama, vLLM and LM Studio
- Supports authenticated scans (login flows, TOTP) and resumable workspaces
Weaknesses
- Needs source code of the target; black-box testing is only in paid Pro and Enterprise
- Actively executes exploits, so it must not be pointed at production systems
- Anthropic and OpenAI cyber safeguards can interrupt scans unless you complete their verification
- Claude Code subscriptions unsupported after version 1.9.0; model API costs are yours
- no GPU
- Docker + Compose
- Needs Docker, Node.js 18+, AI provider API key or subscription
- Models: Anthropic, OpenAI, xAI, AWS Bedrock, Ollama
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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