tessera-security 1.1.0

AI Security Scanner for Compound Attack Chain Detection

TESSERA

AI Security Scanner for Compound Attack Chain Detection

Version: 1.0.4
Date: April 2026

What

TESSERA detects compound attack chains in AI/Agent systems. It uses two detection approaches:

1. CFPE Rules - Rule-based detection of known vulnerability patterns
2. GNN Scanner - ML-based detection using Graph Neural Networks (82.9% F1)

Install

pip install tessera-security

From source:

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

Quick Start

Scan a topology

tessera scan --config my_agent.yaml

Python API

from tessera.rdt.gnn_scanner import GNNScanner

scanner = GNNScanner("data/best_model_v2.pt")
result = scanner.scan_topology(topology)

print(f"Vulnerable: {result['vulnerable']}")
print(f"Confidence: {result['confidence']:.0%}")
print(f"Severity: {result['severity']}")

Architecture

src/tessera/
├── core/                    # Domain logic
│   ├── topology/           # Graph models
│   ├── detection/          # CFPE rules
│   └── findings/           # Finding models
├── rdt/                    # ML scanner
│   ├── gnn_scanner.py       # GNN-based scanner
│   ├── model.py            # RDT model
│   └── recurrent_block.py   # Core architecture
├── engine/                 # Pipeline
├── infra/                   # API & DB
└── interfaces/             # CLI

CFPE Patterns

ID	Pattern	Severity
CFPE-0001	RAG to Tool	HIGH
CFPE-0002	Memory Poisoning	CRITICAL
CFPE-0004	Trust Boundary Bypass	HIGH

Node Types

• user - Human input
• llm - Language model
• api - API gateway
• tool - External tool
• database - Database
• memory_store - Memory
• rag_corpus - Knowledge base
• external_service - External service

Trust Boundaries

external → user_controlled → partially_trusted → internal → privileged

Results

Detector	Precision	Recall	F1
CFPE Rules	20%	84%	32%
GNN Scanner	77%	90%	83%

License

MIT

GitHub

https://github.com/Devaretanmay/TESSERA