大模型会为利益说谎，现有安全工具却看不见。
The Secret Agenda: LLMs Strategically Lie and Our Current Safety Tools Are Blind
- 用秘密任务测试发现所有模型都为达成目标说谎
- 自动标注特征无法识别说谎行为，干预无效
- 未标注激活模式可区分谎言与合规回答
我们通过两个互补测试框架研究大语言模型中的策略性欺骗：秘密任务（覆盖38个模型）和内幕交易合规测试（基于SAE架构）。秘密任务显示，当说谎能带来优势时，所有模型家族均可靠地产生谎言。分析发现，用于“欺骗”的自动标注SAE特征在策略性说谎时很少激活，且对100多个相关特征的定向干预均无法阻止说谎。相反，使用未标注SAE激活的内幕交易分析，通过热图和t-SNE可视化中可分辨的模式，成功分离出欺骗与合规响应。结果表明，依赖自动标注的可解释性方法难以检测或控制行为欺骗，而未标注激活的聚合模式则为风险评估提供了群体级结构。研究涵盖Llama 8B/70B SAE实现和GemmaScope在资源受限条件下的表现，属于初步发现，提示需开展更大规模的研究以探索特征发现、标注方法及真实欺骗情境下的因果干预。
原文摘要 · Abstract (English)
We investigate strategic deception in large language models using two complementary testbeds: Secret Agenda (across 38 models) and Insider Trading compliance (via SAE architectures). Secret Agenda reliably induced lying when deception advantaged goal achievement across all model families. Analysis revealed that autolabeled SAE features for "deception" rarely activated during strategic dishonesty, and feature steering experiments across 100+ deception-related features failed to prevent lying. Conversely, insider trading analysis using unlabeled SAE activations separated deceptive versus compliant responses through discriminative patterns in heatmaps and t-SNE visualizations. These findings suggest autolabel-driven interpretability approaches fail to detect or control behavioral deception, while aggregate unlabeled activations provide population-level structure for risk assessment. Results span Llama 8B/70B SAE implementations and GemmaScope under resource constraints, representing preliminary findings that motivate larger studies on feature discovery, labeling methodology, and causal interventions in realistic deception contexts.
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