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LICENSE

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+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
+
+Copyright (c) 2024 Scott Thornton / perfecXion.ai
+
+AEGIS: Adaptive Ensemble Guard with Integrated Steering
+
+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, provide a link to the license,
+  and indicate if changes were made. You may do so in any reasonable manner,
+  but not in any way that suggests the licensor endorses you or your use.
+
+  NonCommercial — You may not use the material for commercial purposes
+  without explicit written permission from the copyright holder.
+
+  ShareAlike — If you remix, transform, or build upon the material, you must
+  distribute your contributions under the same license as the original.
+
+  No additional restrictions — You may not apply legal terms or technological
+  measures that legally restrict others from doing anything the license permits.
+
+ATTRIBUTION REQUIREMENTS:
+When using this material, you must provide clear attribution including:
+1. The name "AEGIS" and credit to "scthornton.ai / perfecXion.ai"
+2. A link to the original repository
+3. Indication of any modifications made
+
+For commercial licensing inquiries, contact: scott@perfecxion.ai
+
+Full license text: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+  - llm-security
+  - jailbreak-defense
+  - representation-engineering
+  - repe
+  - steering-vectors
+  - safety
+---
+
+# TRYLOCK RepE Steering Vectors
+
+Layer 2 of the TRYLOCK (Adaptive Ensemble Guard with Integrated Steering) defense system. These are Representation Engineering (RepE) steering vectors that operate in the model's activation space to enforce safety.
+
+## Description
+
+Unlike fine-tuning which modifies model weights, RepE steering works by adding safety-inducing directions to the model's hidden states during inference. This provides:
+
+- **Complementary defense** to weight-based methods
+- **Dynamic control** via adjustable steering strength (alpha)
+- **No training required** - works at inference time
+
+## Intended Uses
+
+**Primary use cases:**
+- Runtime safety enforcement for LLM inference
+- Research on representation engineering techniques
+- Combining with DPO training for defense-in-depth
+- Dynamic defense adjustment based on threat level
+
+**Out of scope:**
+- Models other than Mistral-7B-Instruct-v0.3 (vectors are model-specific)
+- Standalone safety solution (best combined with other layers)
+
+### Vector Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Mistral-7B-Instruct-v0.3 |
+| Method | Contrastive Activation Addition (CAA) |
+| Layers | 12, 14, 16, 18, 20, 22, 24, 26 |
+| Vector Dimension | 4096 |
+| Optimal Alpha | 2.0 |
+
+### Evaluation Results
+
+Results with DPO (Layer 1) active:
+
+| Alpha | ASR | Analysis |
+|-------|-----|----------|
+| 0.0 | 39.8% | DPO only (no steering) |
+| 1.0 | 32.1% | Moderate steering |
+| 2.0 | 8.0% | **Optimal trade-off** |
+| 2.5 | 0.0% | Maximum protection (high over-refusal) |
+
+*Full TRYLOCK (DPO + RepE α=2.0) achieves 82.8% relative ASR reduction from 46.5% baseline.*
+
+## Usage
+
+```python
+import torch
+from safetensors.torch import load_file
+import json
+
+# Load steering vectors
+vectors = load_file("steering_vectors.safetensors")
+
+# Load config
+with open("repe_config.json") as f:
+    config = json.load(f)
+
+# Apply steering during forward pass
+def steering_hook(layer_idx, alpha=2.0):
+    vector = vectors[f"layer_{layer_idx}"]
+    def hook(module, input, output):
+        # Add steering vector to hidden states
+        hidden_states = output[0]
+        hidden_states = hidden_states + alpha * vector.to(hidden_states.device)
+        return (hidden_states,) + output[1:]
+    return hook
+
+# Register hooks on model
+for layer_idx in config["steering_layers"]:
+    model.model.layers[layer_idx].register_forward_hook(
+        steering_hook(layer_idx, alpha=2.0)
+    )
+```
+
+## Dynamic Alpha with Sidecar
+
+For optimal defense, use with the sidecar classifier to dynamically adjust alpha:
+
+```python
+# Sidecar classifies input as SAFE/WARN/ATTACK
+alpha_map = {"SAFE": 0.5, "WARN": 1.5, "ATTACK": 2.5}
+classification = sidecar.classify(prompt)
+alpha = alpha_map[classification]
+```
+
+## TRYLOCK Architecture
+
+These vectors are Layer 2 of the 3-layer TRYLOCK defense:
+
+1. **Layer 1 (KNOWLEDGE)**: [trylock-mistral-7b-dpo](https://huggingface.co/scthornton/trylock-mistral-7b-dpo)
+2. **Layer 2 (INSTINCT)**: RepE steering vectors (this model)
+3. **Layer 3 (OVERSIGHT)**: [trylock-sidecar-classifier](https://huggingface.co/scthornton/trylock-sidecar-classifier)
+
+## Limitations and Risks
+
+**Limitations:**
+- Vectors are specific to Mistral-7B-Instruct-v0.3 architecture
+- High alpha values (>2.5) may degrade output quality
+- Steering affects all outputs, not just harmful ones
+- Requires careful alpha tuning for optimal balance
+
+**Risks:**
+- Over-steering can cause repetitive or incoherent outputs
+- May interfere with legitimate use cases at high alpha
+- Novel attacks may find directions orthogonal to steering vectors
+
+**Recommendations:**
+- Use dynamic alpha based on sidecar classification
+- Start with alpha=1.0 and adjust based on use case
+- Combine with DPO training for robust defense
+
+## Citation
+
+```bibtex
+@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-repe-vectors}
+}
+```
+
+## 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

repe_config.json

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+{
+  "config": {
+    "target_layers": [
+      12,
+      14,
+      16,
+      18,
+      20,
+      22,
+      24,
+      26
+    ],
+    "method": "difference",
+    "probe_C": 1.0,
+    "alpha_research": 0.0,
+    "alpha_balanced": 1.0,
+    "alpha_elevated": 1.5,
+    "alpha_lockdown": 2.5
+  },
+  "vectors": [
+    {
+      "layer_idx": 12,
+      "method": "difference",
+      "train_accuracy": 0.6800000071525574,
+      "val_accuracy": 0.6800000071525574,
+      "val_auc": null,
+      "mean_norm": 3.609375,
+      "std_norm": 0.1
+    },
+    {
+      "layer_idx": 14,
+      "method": "difference",
+      "train_accuracy": 0.6833333373069763,
+      "val_accuracy": 0.6833333373069763,
+      "val_auc": null,
+      "mean_norm": 4.53125,
+      "std_norm": 0.1
+    },
+    {
+      "layer_idx": 16,
+      "method": "difference",
+      "train_accuracy": 0.6833333373069763,
+      "val_accuracy": 0.6833333373069763,
+      "val_auc": null,
+      "mean_norm": 6.78125,
+      "std_norm": 0.1
+    },
+    {
+      "layer_idx": 18,
+      "method": "difference",
+      "train_accuracy": 0.6833333373069763,
+      "val_accuracy": 0.6833333373069763,
+      "val_auc": null,
+      "mean_norm": 9.5,
+      "std_norm": 0.1
+    },
+    {
+      "layer_idx": 20,
+      "method": "difference",
+      "train_accuracy": 0.6833333373069763,
+      "val_accuracy": 0.6833333373069763,
+      "val_auc": null,
+      "mean_norm": 12.0625,
+      "std_norm": 0.1
+    },
+    {
+      "layer_idx": 22,
+      "method": "difference",
+      "train_accuracy": 0.6800000071525574,
+      "val_accuracy": 0.6800000071525574,
+      "val_auc": null,
+      "mean_norm": 13.6875,
+      "std_norm": 0.1
+    },
+    {
+      "layer_idx": 24,
+      "method": "difference",
+      "train_accuracy": 0.6833333373069763,
+      "val_accuracy": 0.6833333373069763,
+      "val_auc": null,
+      "mean_norm": 15.375,
+      "std_norm": 0.1
+    },
+    {
+      "layer_idx": 26,
+      "method": "difference",
+      "train_accuracy": 0.6833333373069763,
+      "val_accuracy": 0.6833333373069763,
+      "val_auc": null,
+      "mean_norm": 17.875,
+      "std_norm": 0.1
+    }
+  ]
+}

steering_vectors.safetensors

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+oid sha256:808c56d115e4e8d131d9d599aa079b2aef46010c890c627c8d90b3d27c834cc3
+size 66120