自适应内鬼能反向降低被怀疑概率，挑战传统检测逻辑。
HBEE: Human Behavioral Entropy Engine -- Pre-Registered Multi-Agent LLM Simulation of Peer-Suspicion-Based Detection Inversion
- 用多智能体模拟器测试大模型驱动的自适应内鬼行为。
- 自适应内鬼在60分钟时被怀疑程度低于无辜者，检测失效。
- 结果与预注册预测相反，揭示了新型检测机制的脆弱性。
内鬼检测假设自适应内鬼会留下可识别的行为痕迹。本文在受控多智能体模拟器中，通过大模型驱动的自适应内鬼检验该假设。预注册的五条件研究将防御模式（级联式与盲式UEBA）与攻击者类型（非自适应与自适应OPSEC）交叉，共进行100次运行（经预设排除后保留95次有效）。主要发现为检测倒置：在T_60时刻，自适应内鬼的同行怀疑度中心性显著低于随机选取的无辜者（Cliff's delta = -0.694，95% BCa置信区间[-0.855, -0.519]，Mann-Whitney p << 0.01），而预注册预测方向相反。预注册等价性检验（H2）显示，自适应OPSEC在任一防御模式下均未导致内鬼的UEBA排名发生可检测变化。两种检测信号（同行怀疑图入度与个体UEBA排名）在自适应攻击下解耦。我们明确限定泛化边界：预注册的基尼系数校准检查（H4）失败，HBEE消息暴露基尼系数（0.213）与SNAP Enron参考值（0.730）差异|Delta Gini| = 0.52，超出等价界限5倍。论文提出一个狭窄但惊人的结论：在可控环境中，当自适应OPSEC可作为大模型指令实现时，基于同行怀疑的级联检测会反转。代码、预注册文档、冻结场景、原始日志与分析流程均已开源。
原文摘要 · Abstract (English)
Insider threat detection assumes that an adaptive insider leaves behavioral residue distinguishing them from legitimate users. We test this assumption against an LLM-driven adaptive insider in a controlled multi-agent simulator. Our pre-registered five-condition study isolates defender mode (cascade vs. blind UEBA) crossed with adversary type (naive vs. adaptive OPSEC) plus a no-mole control, across 100 runs (95 valid after pre-committed exclusions). The primary finding is a detection inversion: at T_60, the adaptive mole's suspicion in-degree is statistically lower than a randomly selected innocent agent (Cliff's delta = -0.694, 95% BCa CI [-0.855, -0.519], Mann-Whitney p << 0.01). The pre-registered prediction was the opposite direction. A pre-registered equivalence test (H2) shows adaptive OPSEC produces no detectable shift in the mole's UEBA rank under either defender mode. The two detection signals (peer suspicion graph in-degree and per-agent UEBA rank) decouple under adaptive adversary behavior. We bound generalization explicitly: a pre-registered Gini calibration check (H4) returns FAIL, with HBEE pairwise message-exposure Gini (0.213) diverging from the SNAP Enron reference (0.730) by |Delta Gini| = 0.52, exceeding the equivalence bound by 5x. The paper makes a narrow but surprising claim: in a controlled environment where adaptive OPSEC is implementable as an LLM directive, peer-suspicion-cascade detection inverts. We release the simulator, pre-registration document, frozen scenarios, raw telemetry, and analysis pipeline under an open-source license.
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