tessera-security 2.0.1

AI Security Scanner for Compound Attack Chain Detection

TESSERA

AI Security Scanner for Compound Attack Chain Detection

Version: 2.0.0
Date: April 2026

What

TESSERA detects compound attack chains in AI/Agent systems using CFPE Rules - rule-based detection of known vulnerability patterns. It also supports optional LLM-powered analysis for semantic vulnerability detection.

The primary production surface is the public Python package and CLI. The FastAPI service is supported as a secondary deployment target for container-on-VM environments behind a reverse proxy.

Features

• 10 CFPE Detection Patterns - Comprehensive coverage of AI agent vulnerabilities
• Multiple Output Formats - Text, JSON, SARIF, HTML
• LLM Integration - Optional AI-powered analysis (OpenAI, Anthropic, Ollama)
• CI/CD Ready - GitHub Actions, pre-commit hooks
• MCP Server - Model Context Protocol support

Install

pip install tessera-security

From source:

git clone https://github.com/Devaretanmay/TESSERA.git
cd TESSERA
pip install -e .

Support Matrix

• Supported Python versions: 3.10, 3.11, 3.12
• Operational default: 3.11
• Package + CLI are the primary supported production surface
• FastAPI is a secondary production surface

Quick Start

CLI

# Scan a topology
tessera scan --config my_agent.yaml

# Output formats: text (default), json, sarif, html
tessera scan --config my_agent.yaml --format sarif

# List all detection rules
tessera list-rules

# Explain a specific rule
tessera explain CFPE-0001

Python API

from tessera import Tesseract, OutputFormat

# Simple usage
scanner = Tesseract()
result = scanner.scan("my_agent.yaml", OutputFormat.TEXT)

# JSON output
result = scanner.scan("my_agent.yaml", OutputFormat.JSON)
print(f"Found {result['summary']['total']} vulnerabilities")

# HTML report
result = scanner.scan("my_agent.yaml", OutputFormat.HTML)
with open("report.html", "w") as f:
    f.write(result)

# With LLM analysis (requires API key)
scanner.enable_llm({"provider": "openai"})
result = scanner.scan("my_agent.yaml", OutputFormat.JSON, llm_enabled=True)

Programmatic Topology

from tessera import Graph, Node, Edge, TrustBoundary, DataFlow, detect

graph = Graph(
    system="my_agent",
    nodes={
        "user": Node(id="user", type="user", trust_boundary=TrustBoundary.EXTERNAL),
        "llm": Node(id="llm", type="llm", trust_boundary=TrustBoundary.INTERNAL),
        "tool": Node(id="tool", type="tool", trust_boundary=TrustBoundary.INTERNAL),
    },
    edges=[
        Edge(from_node="user", to_node="llm", data_flow=DataFlow.API, trust_boundary=TrustBoundary.EXTERNAL),
        Edge(from_node="llm", to_node="tool", data_flow=DataFlow.TOOL_CALL, trust_boundary=TrustBoundary.INTERNAL),
    ]
)

findings = detect(graph)

for finding in findings:
    print(f"[{finding.severity.value.upper()}] {finding.id}: {finding.description}")

Output Formats

Text (CLI default)

TESSERA Security Scan
========================================
System: my_agent
Version: 1.0
Graph: 3 nodes, 2 edges
Scan time: 0.05ms

Summary:
  HIGH: 1

Findings:

1. [HIGH] CFPE-0001
   RAG to Tool execution chain detected
   Remediation:
   1. Validate RAG outputs before tool execution
   ...

JSON

{
  "tessera_version": "2.0.0",
  "scan": {
    "system": "my_agent",
    "version": "1.0",
    "scan_time_ms": 0.05,
    "graph": {"nodes": 3, "edges": 2}
  },
  "findings": [...],
  "summary": {"total": 1, "by_severity": {"critical": 0, "high": 1}}
}

SARIF (GitHub Code Scanning)

tessera scan --config my_agent.yaml --format sarif --output results.sarif

Results appear in GitHub Security tab under "Code Scanning".

HTML

Generate beautiful HTML reports:

tessera scan --config my_agent.yaml --format html --output report.html

CI/CD Integration

GitHub Actions

name: CI
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v5
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - run: pip install -e ".[dev,api]"
      - run: python -m ruff check src tests
      - run: python -m pytest -q
      - run: python -m build
      - run: python -m twine check dist/*

Pre-commit Hook

Add to .pre-commit-config.yaml:

repos:
  - repo: https://github.com/Devaretanmay/TESSERA
    rev: v2.0.0
    hooks:
      - id: tessera-scan

LLM Analysis (Optional)

TESSERA supports optional LLM-powered analysis for deeper semantic understanding:

# OpenAI
scanner.enable_llm({"provider": "openai", "model": "gpt-4"})

# Anthropic
scanner.enable_llm({"provider": "anthropic", "model": "claude-3-opus"})

# Ollama (local)
scanner.enable_llm({"provider": "ollama", "model": "llama2"})

Set environment variables:

• OPENAI_API_KEY
• ANTHROPIC_API_KEY

CFPE Patterns (10 rules)

ID	Pattern	Severity	Description
CFPE-0001	RAG to Tool	HIGH	LLM → RAG → Tool chain
CFPE-0002	Memory Poisoning	CRITICAL	Write to persistent memory
CFPE-0003	External to Database	HIGH	Untrusted → database
CFPE-0004	Trust Boundary Bypass	HIGH	Cross-boundary untrusted flow
CFPE-0005	Multi-hop Attack Chain	HIGH	3+ edge attack path
CFPE-0006	Tool to Tool Chaining	MEDIUM	Tool calls tool
CFPE-0007	Sensitive Data Exfiltration	CRITICAL	LLM → external service
CFPE-0008	RAG Context Injection	HIGH	User → RAG injection
CFPE-0009	MCP Config Attack	HIGH	Malicious MCP server
CFPE-0010	Agent Skill Injection	HIGH	SKILL.md compromise

Node Types

• user - Human input source
• llm / model - Language model
• api - API gateway
• tool - External tool/service
• database - Database
• memory_store - Persistent memory
• rag_corpus - Knowledge base (RAG)
• external_service - External API/service
• mcp_server - MCP server
• skill - Agent skill definition

Trust Boundaries

external → user_controlled → partially_trusted → internal → privileged

Architecture

src/tessera/
├── core/                      # Domain logic
│   ├── topology/            # Graph models (Graph, Node, Edge)
│   ├── detection/            # CFPE rules (10 patterns)
│   └── findings/             # Finding models
├── engine/                   # Scanner engine (Tesseract)
├── infra/
│   ├── output/              # Formatters (JSON, SARIF, Text, HTML)
│   ├── llm/                 # LLM providers (OpenAI, Anthropic, Ollama)
│   └── mcp/                 # MCP server
└── interfaces/
    └── cli/                  # CLI commands

Production Docs

• Deployment Guide
• Environment Reference
• Release Playbook
• Support Matrix
• Incident Runbook
• Security Policy

Testing

Run examples:

# All examples
tessera scan --config examples/*.yaml

# Specific example
tessera scan --config examples/complex_agent.yaml --format json

Environment Variables

Variable	Description
OPENAI_API_KEY	OpenAI API key for LLM analysis
ANTHROPIC_API_KEY	Anthropic API key for LLM analysis

License

MIT

GitHub

https://github.com/Devaretanmay/TESSERA