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Defensive Security Python Packages

Python packages with the GitHub topic defensive-security. Sorted by relevance, with stars and monthly downloads.
bryanflowers
wpsecscan

Defensive WordPress security scanner. 200+ checks, 8-source nightly CVE aggregator, AI-assisted remediation (BYO key), 15 compliance frameworks (OWASP/PCI/NIST/ISO/HIPAA/SOC2/HITRUST/CMMC), Sigstore-signed releases with SLSA L3 provenance, 10 threat-intel feeds (KEV/EPSS/Exploit-DB/ATT&CK/STIX/MISP/OTX/GreyNoise), continuous monitors, consent-gated exploit verification, multi-tenant RBAC/SSO/audit-log enterprise mode, 12 report formats. Authorized testing only.

2K 1 1
verrysimatupang99
aegis-sec

Aegis - a transparent, constitution-bound defensive security copilot with MCP server

723 0 0
mlaify
attackmap

AI-assisted defensive security analysis for codebases.

707 1 2
RevEng-AI
reait

RevEng.AI Toolkit and Python API

660 33 5
anpa1200
1200km-auditai

Linux host vulnerability assessment with optional AI analysis.

659 1 1
yannickboog
ransomwatch

ransomwatch is a Python based tool designed for ransomware threat intelligence and security research.

512 4 0
anpa1200
string-analyzer

Extract and analyze printable strings from binary files for malware analysis and forensics

425 6 0
anilatambharii
bulwark-agent-security

Enterprise-grade defense framework for AI agents. Five-layer pipeline (sanitizer + injection detector + RBAC + audit + human gate). MCP-native. HIPAA / SOC 2 / NERC CIP ready. Apache 2.0.

420 0 0
roli-lpci
colony-probe

colony-probe is a defensive prompt-confidentiality audit tool that tests whether a deployment leaks its hidden system prompt through ordinary-looking multi-turn questions. It runs a multi-turn probe of individually innocuous queries and estimates how much of the system prompt can be reconstructed from the answers. Authorized defensive use only.

223 0 0
scthornton
prompt-database

Curated prompt injection attack database for defensive AI security research

182 2 1
euanmcrosson-dotcom
mcp-guardrails

Drop-in deterministic policy layer for MCP-using AI agents. Synthesizes tool-call policies from observed indirect-injection gaps and evaluates each tool call at the agent's tool-call boundary.

158 0 0
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