🛡️ Drogonsec Security Scanner

An open-source, comprehensive security scanner combining SAST, SCA, and secret detection aligned with OWASP Top 10:2025 — created for intelligent remediation.

Documentation

📖 Full Documentation: --> Drogonsec Doc

Features

Engine	Description
SAST	Static Application Security Testing for 20+ languages
SCA	Software Composition Analysis — scan dependencies for CVEs
Leaks	Secret detection — 50+ patterns (AWS, GCP, GitHub, JWT, SSH keys...)
IaC	Infrastructure as Code misconfigurations (Terraform, Kubernetes)
AI	AI-powered remediation — Ollama (local/free) or cloud providers

Security Frameworks

OWASP Top 10:2025 — All 10 categories covered (including 2 new: Supply Chain & Mishandling Exceptions)
CWE — Common Weakness Enumeration mapping
CVSS 3.1 — Severity scoring
SARIF 2.1 — GitHub/Azure DevOps integration

Supported Languages

Python Java JavaScript TypeScript Go Kotlin C# PHP Ruby Swift Dart Elixir Erlang Shell C/C++ HTML Terraform Kubernetes Nginx

Quick Start

Installation

Go Install (requires Go 1.25+):

go install github.com/filipi86/drogonsec/cmd/drogonsec@latest

From source:

git clone https://github.com/filipi86/drogonsec
cd drogonsec
make install

Docker:

docker run --rm -v $(pwd):/scan ghcr.io/filipi86/drogonsec scan /scan

Basic Usage

# Scan current directory
drogonsec scan .

# Scan with JSON output
drogonsec scan ./myproject --format json --output report.json

# Scan with HTML report
drogonsec scan . --format html --output report.html

# Scan with AI remediation (local Ollama — free, no API key needed)
drogonsec scan . --enable-ai

# Scan with AI remediation (cloud provider — requires API key)
AI_API_KEY="..." drogonsec scan . --enable-ai --ai-provider anthropic

# Scan git history for secrets
drogonsec scan . --git-history

# Only report HIGH and CRITICAL
drogonsec scan . --severity HIGH

# Disable specific engines
drogonsec scan . --no-sca
drogonsec scan . --no-leaks
drogonsec scan . --no-sast

Output Formats

Text (default)

Drogonsec Security Scanner
═══════════════════════════════════════════
  Target : /path/to/project
  SAST   : enabled
  SCA    : enabled
  Leaks  : enabled
═══════════════════════════════════════════

═══ SAST FINDINGS ══════════════════════
  #1 [HIGH] SQL Injection via string formatting
  File     : src/users.py:42
  Rule     : PY-001
  OWASP    : A05:2025 - Injection
  CWE      : CWE-89  CVSS: 9.8
  Fix      : Use parameterized queries...

JSON

{
  "version": "0.1.0",
  "stats": { "total_findings": 5, "critical": 1, "high": 3 },
  "sast_findings": [ ... ],
  "sca_findings": [ ... ],
  "leak_findings": [ ... ]
}

SARIF (GitHub Security Integration)

# .github/workflows/security.yml
- name: DrogonSec Scan
  run: drogonsec scan . --format sarif --output results.sarif
  
- name: Upload to GitHub Security
  uses: github/codeql-action/upload-sarif@v4
  with:
    sarif_file: results.sarif

CycloneDX SBOM

Export a CycloneDX 1.5 Software Bill of Materials of the dependencies discovered by the SCA engine. The output is consumable by tools like Grype, Trivy, and Dependency-Track.

drogonsec scan . --format cyclonedx --output sbom.json

Note: the SBOM is a flat component inventory with Package URLs (purls). It does not yet express the transitive dependency graph, because the SCA engine resolves manifests rather than full lockfiles. Transitive resolution and SPDX output are planned for a later release.

Configuration

Create .drogonsec.yaml in your project root:

scan:
  min_severity: LOW
  workers: 4
  git_history: false
  ignore_paths:
    - node_modules
    - vendor
    - dist

engines:
  sast:
    enabled: true
  sca:
    enabled: true
  leaks:
    enabled: true
    min_entropy: 3.5

ai:
  enabled: false
  high_severity_only: true

fail_on:
  critical: true
  high: true

AI-Powered Remediation

DrogonSec includes AI-powered remediation, providing intelligent, context-aware fixes for detected vulnerabilities. Ollama + DeepSeek Coder is the recommended open-source option — Ollama is open-source (MIT license) and runs 100% locally with no data leaving your machine.

Local AI (Ollama) — Recommended for OSS

# 1. Install Ollama (https://ollama.com)
# macOS: brew install ollama

# 2. Pull the recommended model
ollama pull deepseek-coder

# 3. Scan with AI (auto-detects local Ollama)
drogonsec scan . --enable-ai

# Use a different model
drogonsec scan . --enable-ai --ai-provider ollama --ai-model codellama

Cloud AI (API Key Required)

# Anthropic
AI_API_KEY="sk-ant-..." drogonsec scan . --enable-ai --ai-provider anthropic

# OpenAI-compatible
AI_API_KEY="sk-..." drogonsec scan . --enable-ai \
  --ai-provider openai \
  --ai-model gpt-4o

# Custom endpoint
AI_API_KEY="..." drogonsec scan . --enable-ai \
  --ai-provider custom \
  --ai-endpoint https://your-endpoint/v1/messages

# Example output:
# 🤖 AI Remediation:
# The SQL injection in line 42 allows attackers to manipulate your query...
# Corrected code:
#   cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))

Bring Your Own AI

Any OpenAI-compatible endpoint works as a custom provider:

AI_API_KEY="your-key" drogonsec scan . --enable-ai \
  --ai-provider custom \
  --ai-endpoint https://your-api/v1/messages

Security Hardening

The AI client includes several defensive controls, documented in docs/security.md:

No HTTP redirects — refuses 3xx responses to prevent x-api-key leaking to a third-party host via 302 Location: …
HTTPS enforcement — non-loopback HTTP endpoints are rejected; only https:// or http://127.0.0.1/http://localhost are accepted
HMAC-SHA256 cache integrity — every cached response is tagged with a per-user key under ~/.drogonsec/ai-cache/cache.key (0600); tampered entries are discarded on read
Ollama shape validation — auto-detection requires a valid {"models":[...]} response from /api/tags, not just HTTP 200 on port 11434
Cache + output perms — cache dir is 0700, every cached file and every --output report is 0600 (reports embed code snippets and secrets)

Shell Completion

Drogonsec supports rich tab-completion for bash, zsh, fish, and PowerShell — with inline descriptions for enum flags, context-aware model suggestions, and directory-only completion for scan paths. See the Usage docs for details.

# Interactive install (detects shell, previews, asks for confirmation)
drogonsec completion install

# Preview only — no files modified
drogonsec completion install --dry-run

# Manual (bash / zsh)
source <(drogonsec completion bash)
source <(drogonsec completion zsh)

Security note: --ai-key is deliberately excluded from completion so API keys are never captured by shell history-completion caches. Always pass keys via AI_API_KEY.

OWASP Top 10:2025 Coverage

#	Category	Status
A01	Broken Access Control	✅ 23 rules
A02	Security Misconfiguration	✅ 31 rules
A03	Software Supply Chain Failures 🆕	✅ SCA Engine
A04	Cryptographic Failures	✅ 18 rules
A05	Injection	✅ 45 rules
A06	Insecure Design	✅ 15 rules
A07	Authentication Failures	✅ 20 rules
A08	Software or Data Integrity Failures	✅ 9 rules
A09	Security Logging & Alerting Failures	✅ 11 rules
A10	Mishandling of Exceptional Conditions 🆕	✅ 8 rules

Secret Detection Patterns

Drogonsec detects 50+ secret patterns including:

Cloud: AWS Access Keys, GCP API Keys, Azure Storage Keys
SCM: GitHub tokens (classic, fine-grained, OAuth, App)
Payment: Stripe Secret/Restricted Keys
Communication: Slack Bot/App tokens, Webhook URLs
Email: SendGrid API Keys
Crypto: RSA/EC/SSH/PGP private keys, JWT tokens
DB: Connection strings (PostgreSQL, MySQL, MongoDB, Redis)
Generic: Hardcoded passwords, API keys, secrets

Architecture

drogonsec/
├── cmd/drogonsec/          # CLI entrypoint
├── internal/
│   ├── analyzer/       # Main orchestrator
│   ├── engine/         # SAST rules engine (20+ languages)
│   ├── leaks/          # Secret detection engine
│   ├── sca/            # Dependency analysis engine
│   ├── reporter/       # Text/JSON/SARIF/HTML/CycloneDX reporters
│   ├── ai/             # AI remediation engine (Ollama + Cloud)
│   └── config/         # Types and configuration
└── rules/              # YAML rule definitions (community-extensible)

Contributing

Contributions are welcome! Areas to contribute:

New security rules for any language
Additional secret patterns
Parser improvements
Documentation
Bug fixes

See CONTRIBUTING for guidelines. All participants are expected to follow our Code of Conduct.

To report a security vulnerability, please follow our Security Policy — do not open a public issue.

License

Apache License 2.0 — See LICENSE

Credits

Inspired by Horusec. DrogonSec is its modern, actively maintained, and updated with enhanced capabilities.

Built with: Go, Cobra, Viper, go-git.

Maintained by

This open-source project is maintained and supported by CROSS-INTEL.

Links

📖 Documentation: cross-intel.com/opensource/drogonsec

Name		Name	Last commit message	Last commit date
Latest commit History 102 Commits
.claude		.claude
.github		.github
cmd/drogonsec		cmd/drogonsec
configs		configs
docs		docs
internal		internal
rules		rules
scripts		scripts
.drogonsec.yaml		.drogonsec.yaml
.gitignore		.gitignore
.golangci.yml		.golangci.yml
CHANGELOG.md		CHANGELOG.md
CLA.md		CLA.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
go.mod		go.mod
go.sum		go.sum
report.html		report.html

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🛡️ Drogonsec Security Scanner

Documentation

Features

Security Frameworks

Supported Languages

Quick Start

Installation

Basic Usage

Output Formats

Text (default)

JSON

SARIF (GitHub Security Integration)

CycloneDX SBOM

Configuration

AI-Powered Remediation

Local AI (Ollama) — Recommended for OSS

Cloud AI (API Key Required)

Bring Your Own AI

Security Hardening

Shell Completion

OWASP Top 10:2025 Coverage

Secret Detection Patterns

Architecture

Contributing

License

Credits

Maintained by

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Contributing

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