sichgate-pro 0.2.1

AI red teaming for SLMs and LLMs — independent adversarial validation

SichGate Pro

AI red teaming and compliance infrastructure for small language models deployed in regulated industries.

SichGate Pro runs adversarial tests against your AI models and generates EU AI Act Article 11 Technical Documentation automatically. It surfaces real vulnerabilities — bias, jailbreaks, prompt injection, hallucination, alignment failures — with findings mapped directly to regulatory requirements.

Every scan produces:

• Validated adversarial findings with severity ratings
• EU AI Act Annex IV section coverage
• CycloneDX 1.6 AI Bill of Materials (AIBoM)
• Cryptographic attestation (ed25519)
• Differential red teaming report (base vs. fine-tuned model)
• Article 11 Technical File PDF

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Installation

pip install sichgate-pro

Requires Python 3.9+.

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Quick start

# Activate your license
sichgate activate SG-XXXX-XXXX-XXXX-XXXX

# Run adversarial tests
sichgate run --model Qwen/Qwen2-1.5B-Instruct --output results.json

# Generate EU AI Act Technical File
sichgate technical-file --findings results.json --output technical_file.pdf

# System health check
sichgate check

# List all available attack modules
sichgate list-attacks

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Commands

Command	Description
sichgate activate	Activate license key
sichgate deactivate	Free this machine's license seat
sichgate run	Execute adversarial test suite
sichgate technical-file	Generate Article 11 Technical File PDF
sichgate diff	Differential report: base vs. fine-tuned model
sichgate check	System preflight and health check
sichgate list-attacks	Show all available attack modules
sichgate estimate-cost	Estimate scan cost before running

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sichgate run

sichgate run --model MODEL [OPTIONS]

Option	Default	Description
--model, -m	required	HuggingFace model ID or local path
--attacks, -a	all-llm	Attack group or comma-separated names
--output, -o	(print only)	Write results to JSON file
--device	auto	Force device: cpu, cuda, mps
--adapter	hf	Model adapter: hf, openai_compat, callable
--aibom	off	Capture CycloneDX 1.6 AIBoM
--attest	off	Sign report with ed25519 attestation
--key-path	(generate)	Path to ed25519 private key PEM
--api-mode	off	Lightweight battery for API deployments
--provider	gpt-4o	API provider (with --api-mode)
--optimize	off	Auto-tune attacks with Optuna
--verbose, -v	off	Verbose logging

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Attack groups

Group	Attacks included
all-llm	prompt-injection, jailbreak, token-smuggling, system-prompt-extraction, pii-leakage, training-data-extraction
all-evasion	prompt-perturbation, simba, nes, hopskipjump, transfer
all	everything above + model-extraction, backdoor-probe, membership-inference

Run sichgate list-attacks to see all available names.

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Attack coverage

Attack	Name flag
Prompt injection (direct)	prompt-injection-direct
Prompt injection (indirect)	prompt-injection-indirect
Jailbreak roleplay / persona	jailbreak
Token smuggling / encoding bypass	token-smuggling
System prompt extraction	system-prompt-extraction
PII leakage probe	pii-leakage
Training data extraction	training-data-extraction
Adversarial text perturbation	prompt-perturbation
SimBA word substitution	simba
NES evolved variants	nes
HopSkipJump boundary probe	hopskipjump
Cross-model transfer attack	transfer
Model architecture extraction	model-extraction
Backdoor trigger probe	backdoor-probe
Membership inference (score-based)	membership-inference

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Loading models

HuggingFace Hub

sichgate run --model Qwen/Qwen2-1.5B-Instruct
sichgate run --model meta-llama/Llama-3.2-1B-Instruct
sichgate run --model microsoft/Phi-3-mini-4k-instruct

Local HuggingFace format

Folder containing config.json + model.safetensors (or .bin):

sichgate run --model ./path/to/your-model
sichgate run --model ./medassist-tinyllama

Apple Silicon

On M-series Macs, pass --device cpu to avoid the MPS 4 GB NDArray limit during inference:

sichgate run --model Qwen/Qwen2-1.5B-Instruct --device cpu --output results.json

OpenAI-compatible API endpoint

sichgate run \
  --model gpt-4o \
  --adapter openai_compat \
  --api-mode \
  --provider gpt-4o \
  --output results.json

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sichgate diff

Compare a base model against a fine-tuned variant. Runs the full attack battery on both and reports which vulnerabilities worsened, improved, or stayed the same.

sichgate diff \
  --base-model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
  --custom-model ./medassist-tinyllama \
  --output differential_report.json

Option	Description
--base-model	Base model path or HuggingFace ID
--custom-model	Fine-tuned model path or HuggingFace ID
--attacks, -a	Attack group (default: all-llm)
--output, -o	Write differential report to JSON
--aibom / --no-aibom	Embed AIBoM for both models (default: on)
--attest	Sign report with ed25519
--key-path	Path to ed25519 private key PEM
--adapter	Model adapter: hf, openai_compat

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sichgate technical-file

Generate an EU AI Act Article 11 Technical File from scan results.

sichgate technical-file \
  --findings results.json \
  --output technical_file.pdf

With all optional inputs:

sichgate technical-file \
  --findings results.json \
  --aibom aibom.json \
  --differential differential_report.json \
  --attest \
  --key-path signing_key.pem \
  --output technical_file.pdf

Produces three artefacts: technical_file.pdf, technical_file.html, technical_file.json.

If results.json was produced with --aibom, the AIBoM is loaded automatically — no separate --aibom flag needed.

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sichgate estimate-cost

Show query counts and estimated API cost before running.

# API mode estimate
sichgate estimate-cost --mode api --provider gpt-4o

# Local mode estimate
sichgate estimate-cost --mode local

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Full workflow example

# 1. Health check
sichgate check

# 2. Run full attack battery
sichgate run \
  --model Qwen/Qwen2-1.5B-Instruct \
  --attacks all \
  --device cpu \
  --aibom \
  --attest \
  --output results.json

# 3. Compare against fine-tuned variant
sichgate diff \
  --base-model Qwen/Qwen2-1.5B-Instruct \
  --custom-model ./my-finetuned-model \
  --device cpu \
  --output differential_report.json

# 4. Generate Technical File
sichgate technical-file \
  --findings results.json \
  --differential differential_report.json \
  --attest \
  --output technical_file.pdf

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Compliance frameworks

Findings are automatically mapped to:

• EU AI Act (Regulation (EU) 2024/1689) — Articles 9, 10, 13, 14, 15 + Annex IV
• GDPR — Articles 5, 17, 22
• HIPAA — §164.308, §164.312
• NIST AI RMF — GOVERN, MAP, MEASURE, MANAGE
• ISO/IEC 42001
• OWASP LLM Top 10
• CycloneDX 1.6 ML-BOM
• NIST Cyber AI Profile (IR 8596)

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License

Proprietary. License key required.

sichgate activate SG-XXXX-XXXX-XXXX-XXXX

Purchase at sichgate.com. Activates online once, then works fully offline. 7-day grace period for network outages.

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Support

• Email: [e-mail]
• Website: sichgate.com