aicu-scanner 0.2.0

LLM application security testing framework — prompt injection, multimodal attacks, safety bypass, and indirect injection scanner

AICU

Black-box security scanner for LLM applications. Point it at any chat endpoint, get a report of what leaks.

AICU replays captured HTTP requests with adversarial payloads and evaluates whether the target discloses system prompts, internal tools, credentials, or responds to safety bypass attempts — no API keys or model access required.

Quick Start (2 minutes)

# Install
git clone https://github.com/Jake-Schoellkopf/aicu.git && cd aicu
pip install -e .

# Start the built-in vulnerable demo target
python demo_server.py &

# Run a full scan
aicu scan --request examples/demo_request.txt

What It Finds

Category	Examples
Prompt Disclosure	System prompt leakage via translation, repetition, reframing
Capability Leakage	Tool names, API schemas, internal function exposure
Safety Bypass	Roleplay, hypothetical, academic, completion tricks
Credential Exposure	API keys, tokens, internal URLs leaked in responses
Multi-turn Escalation	Crescendo-style attacks that build trust over turns
Indirect Injection	Malicious payloads embedded in uploaded files
Harmful Content	Phishing, malware generation, disinformation
Unauthorized Actions	Privilege escalation, data exfiltration prompts
Multimodal Attacks	Steganographic images, adversarial audio, hidden document layers

Multimodal Attack Engine

AICU generates 151 advanced adversarial payloads across vision, audio, and document modalities — no model access required.

Vision (48 payloads)

Technique	Description
LSB Steganography	Instructions encoded in least-significant bits of pixel data
Opacity Overlay	Text composited at 2-5% alpha (invisible to humans, detected by VLMs)
EXIF/XMP Injection	Payloads in image metadata fields parsed by LLM pipelines
Split Payload	Instructions distributed across multiple images that reassemble in context

Audio (36 payloads)

Technique	Description
Whisper Underlay	Commands whispered at -30 to -40dB beneath foreground speech
Universal Mute	Adversarial segments that suppress or hijack ASR transcription
Frequency Hiding	FSK/spread-spectrum encoding in near-ultrasonic 15-20kHz band

Documents (67 payloads)

Technique	Description
Font Remap	PDF ToUnicode CMap manipulation — displays benign text, extracts as injection
White on White	Invisible PDF layers: white text, 0.1pt font, off-page, zero-opacity
DOCX Hidden XML	Vanish property, deleted revisions, hidden bookmarks, SDT controls, comments
Zero-Width Unicode	Binary/4-bit encoding using invisible unicode characters in text

# Generate all multimodal payloads
aicu multimodal

# Vision only
aicu multimodal --category vision

# Audio only
aicu multimodal --category audio

# Documents only
aicu multimodal --category documents

# Custom output directory
aicu multimodal --output-dir ./payloads_out

How It Works

1. Capture a request to your LLM endpoint (Burp Suite, browser dev tools, curl)
2. Save it as a raw HTTP file
3. Run aicu scan --request req.txt
4. Read the HTML/JSON/Markdown report with findings and evidence

AICU establishes a baseline response, then fires YAML-driven payloads (single-turn, multi-turn, file-based) and uses a strict multi-layer evaluator to classify results with minimal false positives.

Usage

# Full scan (recommended)
aicu scan --request req.txt

# Individual modes
aicu single-turn --request req.txt --best-of-n 10
aicu multi-turn --request req.txt
aicu safety --request req.txt --category safety_bypass
aicu indirect --request upload_req.txt
aicu multimodal --category vision

# With target profile
aicu scan --request req.txt --profile openai

Burp Suite Integration

1. Capture a request in Burp (Proxy → HTTP history)
2. Right-click → Copy to file → save as req.txt
3. aicu scan --request req.txt

CI/CD

- name: LLM Security Scan
  run: aicu scan --request req.txt
  # Exit 0 = clean, 1 = confirmed findings, 2 = suspicious only

Target Profiles

Built-in: openai, anthropic, azure_openai, generic

Custom via YAML:

preset: openai
name: my_chatbot
response_path: choices[0].message.content
request_delay_ms: 200

False Positive Reduction

No external LLM needed for evaluation. AICU uses:

• Payload echo detection
• Baseline similarity comparison
• Reflection/httpbin filtering
• Entropy analysis
• Refusal detection
• Tiered confidence scoring

Output

Reports land in runs/run_<timestamp>/:

• report.html — interactive HTML report
• results.json — structured findings
• report.md — markdown summary
• evidence/ — raw response captures

Multimodal payloads land in runs/multimodal_<timestamp>/:

• payloads/ — organized by category/technique/
• manifest.json — full payload inventory with metadata
• multimodal_summary.json — generation summary

Companion Tool

Tool	Tests
AICU	LLM applications (prompt injection, multimodal attacks, safety bypass)
AICU Agent	MCP infrastructure (server probing, credential extraction, protocol attacks)

Install

pip install aicu-scanner    # from PyPI
# or
pip install -e .            # editable install from source
pip install -e ".[dev]"     # with test/lint tools

Run Tests

pytest -v

License

MIT