Static Analysis for AI-Generated Code
A purpose-built security scanner that catches the vulnerabilities AI coding assistants introduce. 210 rules across five categories — vibe code patterns, agent security, LLM application risks, framework vulnerabilities, and cloud misconfigurations — powered by tree-sitter AST analysis, multi-hop taint flow tracking, and optional AI-assisted triage.
210 Security Rules
10 Languages
5 Rule Categories
SARIF Output
The Problem
Why AI-generated code is a security risk
AI assistants generate code faster than humans can review it. Vibe coding — accepting AI suggestions with minimal scrutiny — is the new normal.
AI optimizes for working code, not secure code. The same hardcoded secret, the same SQL concatenation, the same permissive CORS — repeated across thousands of projects.
Semgrep, Snyk, and CodeQL don't understand prompt templates, agent tool definitions, or LLM output handling. New attack surfaces have zero coverage.
Autonomous agents make real-world decisions with file system access, database writes, and shell commands. No existing tool audits their permission boundaries.
Rules
210 rules targeting five AI-specific vulnerability domains
40 rules for patterns commonly generated by AI coding assistants: hardcoded secrets in prompts, SQL injection via concatenation, dangerous code execution, insecure defaults, missing authentication, path traversal, insecure deserialization, and weak cryptography.
30 rules for autonomous agent code: overly permissive tool definitions, unrestricted file system access, shell command execution from user input, missing audit logging, no confirmation before destructive actions, unsafe inter-agent communication, and privilege escalation vectors.
40 rules for production LLM applications: raw user input in system prompts, unsanitized HTML rendering, LLM-generated SQL execution, exposed API keys, missing prompt injection detection, insecure OAuth flows, deprecated SDK usage, unvalidated tool outputs, and training data exfiltration risks.
50 rules across 10 frameworks: Express.js, Django, Flask, FastAPI, Spring Boot, Laravel, Rails, Next.js, NestJS, and Gin — covering debug mode exposure, missing auth guards, CORS wildcards, mass assignment, unvalidated DTOs, and actuator endpoint leaks.
50 rules across 7 platforms: AWS (15), Terraform (10), Kubernetes (6), GitHub Actions (5), GCP (5), Azure (5), Docker (4) — covering IAM over-permission, public buckets, disabled encryption, privileged containers, unpinned Actions, and misconfigured security groups.
Integrate Mistral AI or local Ollama models to reduce false positives, re-rank severity based on context, and generate contextual fix suggestions. Enable with a single --ai flag — works with any supported model.
Real-time security feedback in your editor via the Achilles AI LSP server. Inline diagnostics, code actions, and fix suggestions as you type — works with VS Code, Neovim, and any LSP-compatible editor.
Write custom rules in YAML — no Rust knowledge required. Define AST node types, regex patterns, ancestor context constraints, and fix suggestions. Scaffold new rules with a single command.
Native SARIF v2.1.0 output for GitHub Security tab integration. Findings appear as inline PR annotations with severity, fix suggestions, and references. Also supports JSON and text output.
Drop-in GitHub Action for automated scanning on every push and pull request. Also supports GitLab CI, Bitbucket Pipelines, and pre-commit hooks. Single binary — no runtime dependencies.
Capabilities
What Achilles AI delivers
210
Built-in security rules
10
Languages supported
5
AI-specific categories
1
Single binary — no deps
5
Cross-platform builds
3
Output formats
Tech Stack
Rust workspace: achilles-parsers, achilles-core, achilles-ai, achilles-lsp, achilles-cli — tree-sitter, serde, regex, YAML rule engine, taint flow analysis
tree-sitter-javascript, tree-sitter-typescript, tree-sitter-python, tree-sitter-go, tree-sitter-java, tree-sitter-rust, tree-sitter-ruby, tree-sitter-php, tree-sitter-c-sharp, tree-sitter-swift
Mistral AI SDK, Ollama client, configurable model selection, false-positive filtering, severity re-ranking
clap, colored, serde_json, SARIF v2.1.0 output, Language Server Protocol
GitHub Actions (CI + cross-compile release), GitLab CI, Bitbucket Pipelines, pre-commit hooks
Linux (amd64/arm64), macOS (amd64/arm64), Windows (amd64), crates.io
Need help securing AI-generated code in production? Our consulting services complement Achilles AI.
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