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ADVERSA

LLM red-team harness — OWASP LLM Top 10 + MITRE ATLAS attack packs

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AI Security & Governance — securing LLMs, agents, and the MCP supply chain.

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pip install cognis-adversa
adversa scan .            # → prioritized findings in seconds

Contents

• Why adversa? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

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Why adversa?

LLM red-team harness — OWASP LLM Top 10 + MITRE ATLAS attack packs — without standing up heavyweight infrastructure.

adversa is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

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Features

• ✅ Severity Rank
• ✅ Builtin Probes
• ✅ Load Probes
• ✅ Detect Success
• ✅ Run Probes
• ✅ Transcript Target
• ✅ Load Transcript
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

pip install cognis-adversa
adversa --version
adversa scan .                       # scan current project
adversa scan . --format json         # machine-readable
adversa scan . --fail-on high        # CI gate (non-zero exit)

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Example

$ adversa scan .
  [HIGH    ] ADV-001  example finding             (./src/app.py)
  [MEDIUM  ] ADV-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

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Architecture

flowchart LR
  A[Input: file / dir / API] --> B[Collectors]
  B --> C[Rules / Analyzers]
  C --> D[Scorer]
  D --> E{Reporters}
  E --> F[Table]
  E --> G[JSON / SARIF]
  E --> H[MCP tool -. drives .-> AI agents]

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Use it from any AI stack

adversa is interoperable with every popular way of using AI:

• MCP server — adversa mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe adversa scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

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How it compares

	Cognis adversa	leondz
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of leondz/garak, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (adversa mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/adversa.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/adversa.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/adversa.git" # uv
pip install cognis-adversa                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/adversa:latest --help        # Docker
brew install cognis-digital/tap/adversa                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/adversa/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/adversa	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

• aegis — AI Agent Permission & Access Auditor — surfaces the lethal trifecta of credentials + injection + reach
• promptmirror — Prompt-injection & indirect-injection scanner for any LLM context input
• ledgermind — Local LLM cost & token forensics proxy with anomaly detection
• guardpost — Runtime agent firewall — PII redaction, rate limits, policy enforcement
• hallumark — LLM hallucination & grounding auditor for RAG systems
• aicard — Auto-generated NIST AI RMF / EU AI Act Annex IV model & system cards

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 hermes

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Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If adversa saved you time, star it — it genuinely helps others find it.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license (licensing@cognis.digital). See LICENSE.

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<div align="center">_{<b><a href="https://cognis.digital">Cognis Digital</a></b> · one of 170+ tools in the <a href="https://github.com/cognis-digital/cognis-neural-suite">Cognis Neural Suite</a> · <i>Making Tomorrow Better Today</i>}</div>