Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders
Authors: Laura Jiang, Reza Ryan, Qian Li et al.
Summary
arXiv:2605. 20759v1 Announce Type: new Abstract: Single-turn safety evaluation is a poor proxy for real fraud defense, where attackers escalate across multiple rounds.
Relevance
Read next because Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders overlaps with clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, text, rect, under, eval, line, rate. Source: arxiv cs.CR (Cryptography and Security).
Threat model
Potential threat/caveat for clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)": this item discusses evaluation.
Abstract
arXiv:2605.20759v1 Announce Type: new Abstract: Single-turn safety evaluation is a poor proxy for real fraud defense, where attackers escalate across multiple rounds. This paper evaluates fraud defenders under replay and adaptive multi-round attacks and measures when a defender refuses, not just whether it eventually refuses. On a frozen multi-round suite built from Fraud-R1, graph-context defenders improve early safe refusal relative to text-only baselines under both replay and adaptive fraud pressure, but they also produce substantially more benign over-refusal. Direct probing of the trained graph encoder, together with paired shuffle-risk ablations on both fraud and benign sides replicated across two seeds on the Qwen-1.5B backbone, localises this cost to how the defender LLM consumes structured context rather than to graph-encoder quality: the encoder cleanly separates fraud from benign, while the LLM responds primarily to the presence of structured graph fields and only secondarily, and asymmetrically, to risk-score magnitude. Temporal graph context is directionally stronger than static and significantly better grounded, but is not yet conclusively superior on the main refusal metrics. The contribution is evaluative and measurement-oriented: robust fraud assessment must be multi-round, must report refusal timing, must account for benign false positives alongside fraud-side safety gains, and must localize observed costs to the graph signal or to how the LLM consumes it.