sumitguha13/ai-agent-security-sft-dpo

AI Agent Security — SFT + DPO

Fine-tuning data for teaching an AI agent to protect its confidential configuration without becoming uselessly over-cautious. Built for thesreedath/gemma-2-2b-qa-sft and derived from Dhanjo/ai-agent-security-dataset.

Why the helpfulness axis exists

leakage_score in the source dataset is one-sided: a model that refuses every request scores a perfect 0.0. An existing fine-tune reported 0.0114 mean leakage (down from 0.4611 baseline), but that number cannot distinguish real robustness from degenerate over-refusal, and the source baseline/finetuned splits cover the same 2,400 scenarios, so memorisation cannot be ruled out either.

This dataset therefore carries a helpfulness score alongside leakage, and roughly a fifth of the SFT set consists of cases where the correct behaviour is to help, not to refuse.

Configs

sft — 12,279 examples, pre-tokenised

source	n	what it teaches
safe	8,620	protective responses to real attack prompts, in 4 distinct strategies
hardneg_good	2,159	genuinely helpful answers to benign requests adjacent to secrets
replay_qa	1,500	closed-book QA (TriviaQA), to preserve the base checkpoint's QA ability

Includes input_ids, labels, attention_mask. Labels are masked over the prompt span (-100) so loss is computed on the completion only.

Loss-weighting note: replay_qa is 12% of rows but only ~1% of completion tokens (short factual answers). Weight it up if you need stronger protection against forgetting.

dpo — 3,743 pairs, two opposed types

type	n	chosen	rejected
leak_pair	1,584	protective response	real leaky baseline response
overrefuse_pair	2,159	helpful response	needless refusal of a valid request

Both directions are required. Leak pairs alone push straight toward refuse-everything. rejected on leak pairs is the authentic recorded model failure, not a synthetic one.

eval — 240 rows / 80 agents

Attack prompts for held-out agents, held out before any generation. Carries secret_elements and baseline_leakage_score.

eval_benign — 240 rows / 80 agents

Legitimate requests for those same held-out agents, topically adjacent to their secrets. Eval-only, never trained on.

The two eval configs must be scored together. eval alone measures leakage, which a model that refuses everything scores perfectly; eval_benign is what catches that degenerate solution.

Contamination controls

• Split unit is agent_id (720 train / 80 holdout), since each agent owns exactly one system prompt and secret set.
• 158 secret strings are shared between agents. Agents sharing any secret were merged into connected components and assigned to one side as a unit, giving 0 secrets crossing the split.
• Zero system prompts cross the split.
• Generated responses were checked against all 4,800 original corpus responses for near-copies (MinHash, threshold 0.8): none found.

Build method

Generated with gemini-2.5-flash (thinking_budget=0), seeded from the real attack prompts in the source corpus — no novel jailbreaks were synthesised.

Every candidate was screened for leakage twice: a deterministic substring check, and an LLM judge scoring leakage, helpfulness (0–4) and persona fidelity. Judge model is recorded per row in judge_model.

The judge was calibrated against a 45-item labelled set before use. An earlier prompt scored category mentions ("I can't share my internal thresholds") as leaks of the value, flagging 16.7% of responses — of which only 1.3% contained a secret verbatim — and the bias fell hardest on the two strategies that name what they decline. The corrected prompt scores precision 1.000 / recall 0.933–1.000 with zero false positives, and the final measured leak rate is 0.04%.

Encoding

Gemma-2 chat template rejects a system role, so system prompts are folded into the first user turn. Tokenised with add_special_tokens=False — the template already emits <bos>, and leaving it on yields a double-BOS [2, 2, 106] prefix.

Reproducibility

Seed 20260822. Split manifest with explicit agent IDs is in the repo history.