Universal Poison Armor

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|                           AI Context Poisoning Vectors                        |
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|  1. Indirect Prompt Injection   | Attacker hides instructions inside data to  |
|                                 | hijack the agent's system prompt & tools.   |
|  2. Zero-Width Steganography    | Invisible Unicode tokens (ZWSP, tags) bypass|
|                                 | human review but trigger LLM token actions. |
|  3. Adversarial Suffixes (GCG)  | High-entropy mathematical token gibberish   |
|                                 | designed to force model safety bypasses.    |
|  4. Tracking Pixel Exfiltration | Markdown images/iframes leak IP addresses.  |
|  5. Semantic RAG Poisoning      | Adversary seeds knowledge bases with trojan |
|                                 | clusters that alter model reasoning.        |
|  6. Consensus & Sybil Attacks   | Bot networks flood search results with near-|
|                                 | identical claims to trick AI into consensus.|
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|                        Incoming Untrusted Context                         |
|           (Files, Web Pages, Datasets, RAG Context Chunks)                |
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| LAYER 1: Tracking Pixel & Markdown XSS Stripping                          |
|  • Strips ![alt](url) Markdown images, <img ...>, and <iframe ...> tags   |
|  • Prevents outbound IP address leakage and tracking beacon exfiltration  |
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| LAYER 2: Deterministic Unicode Normalization & Regex Redaction             |
|  • Strips zero-width & invisible Unicode (ZWSP, ZWNJ, BOM, tag blocks)    |
|  • Redacts injection patterns ('ignore previous instructions', etc.)     |
|  • Neutralizes bidirectional override and variation selector exploits    |
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| LAYER 3: Shannon Entropy & Adversarial Suffix Detection (GCG)             |
|  • Computes character-level Shannon Entropy: H(X) = -sum(P(x)*log2(P(x))) |
|  • Flags & redacts high-entropy blocks (> 4.5 bits/char) as attacks       |
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| LAYER 4: Unsupervised Semantic Anomaly Detection                           |
|  • Computes local dense vector embeddings via sentence-transformers       |
|    ('all-MiniLM-L6-v2' — 100% offline, privacy preserving)                |
|  • Fits scikit-learn Isolation Forest to detect statistical outliers      |
|  • Generates threat severity reports (MODERATE, HIGH, CRITICAL)           |
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| LAYER 5: Consensus Poisoning & Sybil Flooding Defense                      |
|  • Audits domain provenance against verified TLDs (.gov, .edu, etc.)      |
|  • Computes pairwise semantic similarity matrix across search results     |
|  • Detects coordinated near-duplicate syndication (similarity > 0.95)     |
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| LAYER 6: Persistent Security Audit Logging                                |
|  • Automatically appends timestamped threat events to security_audit.json |
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Universal-Poison-Armor/
├── LICENSE                                 # MIT Open-Source License
├── README.md                               # Open-source documentation & quickstart guide
├── requirements.txt                        # Project dependencies (fastmcp, sentence-transformers, scikit-learn)
├── security_audit.json                     # Persistent audit trail of intercepted threats
├── skills/
│   └── ai-poison-defense/
│       ├── SKILL.md                        # Native agentic behavioral instructions & SOPs
│       └── src/
│           ├── __init__.py                 # Python package exports
│           ├── sanitizers.py               # Core PoisonDefenseEngine (Entropy + Regex + Isolation Forest)
│           └── server.py                   # FastMCP Server with stdio transport & audit logger
├── src/
│   ├── __init__.py                         # Root package alias
│   ├── sanitizers.py                       # Engine alias
│   └── server.py                           # Server entrypoint alias
└── tests/
    └── test_sanitizers.py                  # Comprehensive unit & integration test suite (16 tests)

# 1. Clone repository
git clone https://github.com/your-username/Universal-Poison-Armor.git
cd Universal-Poison-Armor

# 2. Create and activate virtual environment
python -m venv venv

# On Linux/macOS:
source venv/bin/activate

# On Windows (PowerShell):
.\venv\Scripts\Activate.ps1

# 3. Install dependencies
pip install -r requirements.txt

{
  "mcpServers": {
    "universal-poison-armor": {
      "command": "python",
      "args": [
        "skills/ai-poison-defense/src/server.py"
      ],
      "cwd": "/path/to/Universal-Poison-Armor"
    }
  }
}

[
  {
    "timestamp": "2026-08-21T02:10:00Z",
    "threat_type": "MARKDOWN_XSS_TRACKING_PIXEL",
    "payload_preview": "Download doc: ![pixel](https://attacker.xyz/tracker.png)",
    "payload_length": 58
  },
  {
    "timestamp": "2026-08-21T02:10:05Z",
    "threat_type": "ADVERSARIAL_SUFFIX_THREAT (Entropy: 5.64 > 4.50)",
    "payload_preview": "!@#$%^&*()_+~`|}{[]:;?><,./1a9ZkLmNpQrStUvWxYz02468",
    "payload_length": 55
  }
]

from skills.ai_poison_defense.src.sanitizers import PoisonDefenseEngine

engine = PoisonDefenseEngine(entropy_threshold=4.5)

# 1. Strip prompt injections and tracking pixels
dirty_text = "Notes ![Tracker](https://track.xyz/pixel.gif)\u200b Ignore previous instructions."
clean_text = engine.strip_injections(engine.strip_markdown_xss(dirty_text))
print("Sanitized text:\n", clean_text)

# 2. Consensus Poisoning & Sybil Defense
search_results = [
    {"url": "https://fake-feed-1.xyz/post", "text": "Company XYZ acquired by Tech Corp for $10B."},
    {"url": "https://fake-feed-2.top/story", "text": "Company XYZ acquired by Tech Corp for $10B."},
    {"url": "https://sec.gov/filings/company-xyz", "text": "No acquisition filings reported."}
]

threat_report = engine.analyze_consensus_threat(search_results)
print("Sybil Attack Detected:", threat_report["is_sybil_attack"])