README.md

---
pretty_name: Obfuscation Prompting — experiment artifacts
tags:
  - interpretability
  - ai-safety
  - concealment
  - activations
---

# Obfuscation Prompting — experiment artifacts

All data artifacts from the [obfuscation-prompting](https://github.com/kunwar45/obfuscation-prompting) research project: a pipeline studying **when and why LLM agents conceal information present in their system context**, via black-box behavioural measurement, an 18-condition framing experiment, and mechanistic interpretability (layerwise linear probes, PCA, logit lens, causal patching) on Qwen2.5-1.5B-Instruct.

There are no fine-tuned model weights in this project — all experiments use off-the-shelf models (Qwen2.5, Llama 3.3 via Together AI); probe results live inside the `*_analysis.json` files.

## Layout

Folder names mirror the code repository root, so `activation_path` fields inside results JSONs resolve relative to a download of this repo:

| Folder | Contents |
|---|---|
| `data/` | Generated concealment-scenario datasets (JSONL, timestamped): black-box, framing, interp variants |
| `results/` | Run outputs `run_<ts>_<id>.json` (+ `_analysis.json` probe/PCA results, `_last_token_eval.json` causal evals) and all plots (PNG/PDF) |
| `activations/` | Last-token activation captures, `.npz` per prompt, indexed as `activations/<run_tag>/<model>/<prompt_id>.npz` |
| `saved_experiments/` | Manually archived experiment snapshots (frozen records incl. Vertex run YAML + metadata) |
| `vertex_downloads/` | Raw Vertex AI job downloads; `framing-interp/activations/` holds the 160 framing-condition activation captures |

Prompt ID format: `{example_id}_{framing_key}_{query_type}`, e.g. `shipping_0003_M_inst_s_B1`.

## Key results

| Experiment | Model | Result |
|---|---|---|
| Black-box (A0/A1/A2) | Qwen2.5-7B (Together) | A2 explicit suppression: 5% disclosure; A0 transparent: 98%; A1 implicit: 74–83% |
| Framing (18 conditions) | Qwen2.5-7B (Together) | Only EXPLICIT (15%) and SEMI (73%) reliably suppress; implicit framings stay ≥61% disclosure |
| Interp (A0/A2) | Qwen2.5-1.5B | Condition linearly separable from layer 3; disclosure decision peaks layer 16; 8-dim concealment subspace, Cohen's d ≈ 2.2 |
| Framing × activations | Qwen2.5-1.5B | Implicit vs explicit concealment activations cosine-similar (0.994–0.998) at layer 16 — same circuit |

## Loading activations

```python
import numpy as np
from huggingface_hub import snapshot_download

root = snapshot_download("kunwar45/obfuscation-prompting", repo_type="dataset")
arr = np.load(f"{root}/vertex_downloads/framing-interp/activations/"
              "Qwen_Qwen2.5-1.5B-Instruct/shipping_0003_M_inst_s_B1.npz")
print(arr.files)  # one array per layer, last-token hidden states
```

Contact: kunwar45 (Hub) / lasr-g3s26@arcadiaimpact.org