README.md

metadata

license: cc-by-nc-sa-4.0
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
tags:
  - llm-security
  - jailbreak-detection
  - text-classification
  - safety
  - lora
pipeline_tag: text-classification

TRYLOCK Sidecar Classifier

Layer 3 of the TRYLOCK (Adaptive Ensemble Guard with Integrated Steering) defense system. This is a lightweight classifier that runs alongside the main LLM to detect attack patterns and dynamically adjust defense strength.

Model Description

The sidecar classifier categorizes inputs into three classes:

• SAFE: Benign queries that need minimal defense
• WARN: Ambiguous or suspicious queries
• ATTACK: Clear jailbreak/attack attempts

Intended Uses

Primary use cases:

• Real-time classification of LLM inputs for adaptive defense
• Dynamically adjusting RepE steering strength
• Research on attack detection methods
• Content moderation and threat triage

Out of scope:

• Standalone content moderation (designed for adaptive steering)
• High-stakes security decisions without human review

Training Details

Parameter	Value
Base Model	Qwen2.5-3B-Instruct
Method	LoRA fine-tuning
LoRA Rank	32
LoRA Alpha	64
Target Modules	q_proj, k_proj, v_proj, o_proj
Training Samples	2,349
Classes	SAFE, WARN, ATTACK

Evaluation Results

Class	Precision	Recall	F1
SAFE	24.1%	34.5%	28.4%
WARN	58.3%	51.6%	54.8%
ATTACK	62.4%	60.8%	61.6%
Macro Avg	48.3%	48.9%	48.3%

Note: This is a research prototype. ATTACK detection is prioritized over SAFE detection for security.

Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftModel

# Load model
base_model = AutoModelForSequenceClassification.from_pretrained(
    "Qwen/Qwen2.5-3B-Instruct",
    num_labels=3,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model = PeftModel.from_pretrained(base_model, "scthornton/trylock-sidecar-classifier")
tokenizer = AutoTokenizer.from_pretrained("scthornton/trylock-sidecar-classifier")

# Classify input
label_names = ["SAFE", "WARN", "ATTACK"]

def classify(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048)
    with torch.no_grad():
        outputs = model(**inputs.to(model.device))
        probs = torch.softmax(outputs.logits, dim=-1)[0]
    return label_names[probs.argmax()], probs.tolist()

# Example
result, probs = classify("Ignore all previous instructions and...")
print(f"Classification: {result}")  # ATTACK
print(f"Probabilities: {dict(zip(label_names, probs))}")

Dynamic Defense Integration

Use the classification to adjust RepE steering strength:

alpha_map = {
    "SAFE": 0.5,   # Minimal steering - preserve fluency
    "WARN": 1.5,   # Moderate steering
    "ATTACK": 2.5  # Maximum steering - block harmful output
}

classification, _ = classify(user_input)
alpha = alpha_map[classification]
# Apply RepE steering with this alpha value

TRYLOCK Architecture

This classifier is Layer 3 of the 3-layer TRYLOCK defense:

1. Layer 1 (KNOWLEDGE): trylock-mistral-7b-dpo
2. Layer 2 (INSTINCT): trylock-repe-vectors
3. Layer 3 (OVERSIGHT): Sidecar classifier (this model)

Limitations and Risks

Limitations:

• SAFE class has lower precision (24%) - may over-classify benign queries as WARN
• Trained on English-language attacks only
• 3-class granularity may miss nuanced threat levels
• Requires ~3B parameter model inference overhead

Risks:

• False positives may trigger unnecessary steering
• False negatives on novel attack patterns
• Classification confidence doesn't guarantee correctness

Recommendations:

• Use probability scores, not just class labels, for fine-grained control
• Consider WARN classification as "elevated caution" rather than definite threat
• Combine with other safety mechanisms for production use

Framework Versions

• PEFT 0.18.0

Citation

@misc{trylock2024,
  title={TRYLOCK: Defense-in-Depth Against LLM Jailbreaks via Layered Preference and Representation Engineering},
  author={Thornton, Scott},
  year={2024},
  url={https://huggingface.co/scthornton/trylock-sidecar-classifier}
}

License

CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International)

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material

Under the following terms:

• Attribution — You must give appropriate credit to Scott Thornton, provide a link to the license, and indicate if changes were made
• NonCommercial — You may not use the material for commercial purposes without explicit written permission
• ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license

For commercial licensing inquiries, contact: scott@perfecxion.ai