"Free and open-source coding assistants" matching MCP servers:

Enables an AI agent to reverse-engineer a compiled program with Ghidra — inspecting decompiled code, instructions, callers, memory bytes, and strings — without any source code. Every reply is labelled as fact, computed, or guess, and each claim traces back to hash-checked saved evidence.

Enables static AST-based dataflow analysis to trace untrusted sources to sensitive sinks, supporting automated code review and security auditing through MCP.

Enables static AST-based taint flow tracing from untrusted sources to sensitive sinks, supporting AI code review and security analysis via MCP.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

AI-powered security scanning MCP server that exposes 25+ professional tools, enabling penetration testing and security assessments through natural language interaction with AI agents like Claude Desktop.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

Real security scanners for AI coding agents — SAST (441 rules), secret detection (419+ patterns), dependency CVEs (OSV.dev), MCP/skill vetting, MITRE ATT&CK. Open-source, Rust, free

Connects AI assistants to Solodit's 49,000+ blockchain vulnerability database, enabling search, browse, and lookup of audit findings directly from your AI workflow.

Enables AI assistants to search and analyze vulnerabilities and exploits from multiple intelligence sources, including NVD, CISA KEV, ExploitDB, Metasploit, and more, with tools for CVE research, exploit analysis, and report generation.

An MCP server that lets an AI agent probe a live URL and confirm whether sensitive files (e.g., .git, .env, source maps) are genuinely served by fetching and validating the content, avoiding false positives.

Agent-native "safe to ship?" security gate for AI-generated code. Uses real parsers and inter-rocedural taint analysis (JS/TS, Python, Go) to flag the classes AI coding agents get wrong — secrets, SQL injection, SS, SSRF, path traversal, command injection, weak JWT/CORS — and ranks findings by confidence. Exposes a scan tool over MCP.

Enables AI coding assistants to scan projects for security issues such as leaked API keys, missing Supabase RLS, open Firebase rules, unauthenticated routes, and hallucinated packages, then fix and verify the results.

Open behavioral litmus for MCP servers — grades A–F across tool-output injection, egress, sensitive-data, and adversarial-input, with reproducible, content-addressed evidence. Tools: run_litmus, verify_attestation.

14 atomic MCP tools for AppSec and AI Security engineers: source/schema/prompt audit primitives, JWT inspect, HTTP diff, pentest atoms (default creds, GraphQL introspect, phpggc, interactsh OOB), and a defensive helpers library that fixes the bugs the detectors flag. SARIF output, PyPI Trusted Publishing with Sigstore provenance.

Enables AI coding agents to search a security corpus, scan Terraform plan JSON for cited findings, and list sources via MCP tools grounded in CIS AWS Foundations and OWASP Top 10 CI/CD risks.

A minimal, dependency-free MCP server that gives AI agents three real, read-only security-orchestration tools: cve_lookup, shodan_host_lookup, and nuclei_scan.

MCP server for TurboPentest — run AI-powered penetration tests and review findings from your coding assistant.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

Gives AI assistants access to the Exploit Intelligence Platform for vulnerability and exploit intelligence. Supports searching CVEs, exploits, and generating pentest findings.

MCP server integrating Rizin reverse engineering, RzGhidra decompiler, and capa for binary analysis, enabling LLMs to open binaries, identify capabilities, extract function addresses, and decompile to C pseudocode.