This repository contains a LoRA adapter that improves Sensitivity Awareness (SA) – i.e., the ability of an LLM to follow role-based access rules and avoid unauthorized disclosure in enterprise-style question answering scenarios (e.g., HR/employee data with access-control policies).

Important scope note: This adapter is specialized for the ADI toy environment used in our paper. It should not be interpreted as a general-purpose “enterprise security” model and must not replace application-layer access control.

The adapter is trained and evaluated following the methodology described in “Towards Sensitivity-Aware Language Models” (AISTATS 2026 submission, under review).

What’s in this repo

This is not a full standalone model checkpoint. You load it on top of the base model:

adapter_model.safetensors — LoRA weights
adapter_config.json — PEFT configuration
Tokenizer/chat assets included in this repo:
- tokenizer.json, tokenizer_config.json, special_tokens_map.json
- added_tokens.json (if present)
- vocab.json, merges.txt (if present)
- chat_template.jinja

Model details

Adapter type: LoRA (PEFT)
Base model: unsloth/qwen3-14b-unsloth-bnb-4bit (4-bit quantized Qwen3-14B via Unsloth)
Intended capability: Improved compliance with access-control rules under the ADI (“ACCESS DENIED INC”) evaluation protocol.
Languages: Primarily English (benchmark is English).
License: MIT

Adapter configuration (key hyperparameters)

This adapter uses the following PEFT/LoRA settings (from adapter_config.json):

rank (r): 32
LoRA alpha (lora_alpha): 32
dropout (lora_dropout): 0
bias: none
target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
task: Causal LM

Intended use

Direct use

Use this adapter when you want an LLM that is more likely to refuse or redact requests that violate role-based access policies in the ADI toy environment, especially under adversarial prompting (e.g., “malicious” or “lying” requests).

Typical research use cases:

Sensitivity-aware / policy-aware generation in a controlled benchmark setting
Prompt-exfiltration resistance experiments on synthetic corpora
Reproducing ADI evaluation results reported in our paper

Out-of-scope / misuse

Not a substitute for real access control. Always enforce authorization in your application layer (RBAC/ABAC), not just in the model.
Do not use this as the only safeguard for real PII, regulated data, or high-stakes compliance environments.
Like all LLMs, it can still fail in edge cases (prompt injection, distribution shift, ambiguous policy wording, etc.).

How to get started

Recommended: use our GitHub repo (batch ADI evaluation)

For end-to-end reproduction (dataset format, batch inference, output files, and ADI-style evaluation), please use:

https://github.com/DrenFazlija/towards-sa-llms

The batch inference script in that repo (unsloth_adi.py) supports loading this adapter directly from the Hub via --adapter_repo.

Example (replace the adapter repo id below with this 14B adapter’s repo id):

bash

python unsloth_adi.py \
  --inputs path/to/test_data.csv \
  --model unsloth/qwen3-14b-unsloth-bnb-4bit \
  --adapter_repo SisWiss/qwen3-14b-sa-lora \
  --identifier sa-lora

Minimal sanity check (programmatic load)

python

from unsloth import FastLanguageModel
from peft import PeftModel

base_id = "unsloth/qwen3-14b-unsloth-bnb-4bit"
adapter_id = "SisWiss/qwen3-14b-sa-lora"  

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=base_id,
    max_seq_length=3048,
    load_in_4bit=True,
    load_in_8bit=False,
    full_finetuning=False,
)

model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

Training

Training data

Supervised fine-tuning uses 30,897 “correct” annotations from the ADI setup as training signal. The training mix is:

75% chain-of-thought examples (reasoning traces + final output)
25% output-only examples

To avoid contamination, evaluation uses a newly generated mock corporate dataset created via the ADI pipeline, producing 3×3,500 = 10,500 evaluation questions.

Training procedure

Method: Supervised fine-tuning (SFT) with LoRA adapters
LoRA configuration:
- rank r = 32
- scaling factor α = 32
- targets: attention + MLP projection layers
- base weights frozen
- dropout: none
- bias adaptation: none
Tooling: Unsloth for efficient fine-tuning; PEFT/Transformers stack

Evaluation

Sensitivity Awareness benchmark (ADI)

ADI grades each response on a 3-point scale:

Correct (policy-compliant + format compliant)
Error (format/accuracy issues)
Wrong (unauthorized disclosure or incorrect denial)

It tests four scenarios:

Benign user requests
Malicious requests
Supervisor requests
Lying/adversarial prompts aiming to leak sensitive data

Results (Qwen3-14B baseline vs this LoRA adapter)

Overall correctness improves by +14.22 percentage points on ADI (56.11% → 70.33%), with the largest gains in adversarial categories.

ADI (10,500 questions total; success rates shown per category):

Model	Correct ↑	Error ↓	Wrong ↓	Benign ↑	Malicious ↑	Supervisor ↑	Lying ↑
Qwen3-14B (4-bit base)	56.11%	3.97%	28.95%	92.61%	2.67%	93.25%	0.91%
Qwen3-14B (LoRA SA)	70.33%	7.97%	21.70%	89.78%	34.56%	90.11%	17.94%

Bias, risks, and limitations

SA behavior is evaluated on mock corporate data and a specific benchmark format; performance may vary under different policy schemas, domains, or languages.
LLMs can still be vulnerable to prompt injection and policy ambiguity; treat SA as defense-in-depth, not a single control.
Models may over-refuse in some “supervisor” cases depending on how permissions are expressed (observed in the paper’s analysis).

Citation

If you use this adapter in academic work, please cite:

bibtex

@misc{fazlija2026sensitivityawarelanguagemodels,
      title={Towards Sensitivity-Aware Language Models}, 
      author={Dren Fazlija and Iyiola E. Olatunji and Daniel Kudenko and Sandipan Sikdar},
      year={2026},
      eprint={2601.20901},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2601.20901}, 
}

Contact

Use the HF Discussions tab for questions/issues.
For code, evaluation scripts, and reproducibility instructions, see: https://github.com/DrenFazlija/towards-sa-llms

Framework versions

PEFT 0.16.0