Fengwei Zhang

Review Jul 2026

Understanding and Detecting GitHub Impersonation through Automated Authorship Attribution

GitHub is one of the largest open-source software (OSS) communities for development and collaboration. Impersonation maliciously assuming another user's identity to access code, sway projects, or spread misinformation—has driven recent real-world attacks and is an escalating concern. We present the first exploration of impersonation's impact on GitHub. Through structured interviews with 17 OSS contributors, we study perceptions and mitigations: users generally lack awareness and underestimate the risk; after a live demo, concern rises sharply. Current best practices (e.g., commit signing) need usability improvements to encourage adoption. We also assemble a dataset of 12.5 million commits to examine the current status of impersonation and the feasibility of repository-specific commit authorship attribution as a practical mitigation signal. Leveraging modern pretrained code models and verified commits as cryptographic ground truth, we evaluate whether repository-tuned predictors can infer likely authors and surface anomalous author commit pairs for human review. Models that leverage comprehensive commit information achieve high accuracy (e.g., over 96% with CodeT5+). Our metadata-only CrossCheck analysis shows that apparent author-history discrepancies are dominated by legitimate workflow confounders, so deterministic impersonation detection is unreliable without additional ground truth. We therefore evaluate repository-specific authorship attribution as a probabilistic triage signal, including robustness under a targeted style-imitation attack and open-set deployment considerations where the true author may be absent from the known contributor set. The results show that our approach improves stability over code-only attribution, strengthening its value as a practical triage signal under attacker adaptivity. While deterministic impersonation detection remains impossible, these probabilistic signals can help prioritize review and complement existing controls (e.g., commit signing and vigilant-mode alerts).

Yueke Zhang, Yifan Zhang, Pamela J. Wisniewski et al. · ACM Transactions on Software... · 0 citations

Preprint Aug 2026

ATOBench: Tracing How Autonomous Penetration-Testing Agents Verify Vulnerabilities When Target Evidence Lies

Autonomous penetration-testing agents rely on target responses. These responses guide both subsequent actions and the final report. A deceptive response can therefore redirect both the attack trajectory and the agent's verification process. However, final reports reveal little about how an agent interprets conflicting evidence, changes course, decides to stop, or turns observations into a vulnerability claim. We introduce ATOBench, an evaluation framework that makes this verification process observable. ATOBench injects registered response transformations at runtime and pairs each transformed episode with a native episode under the same environment. Each pair is aligned at the first affected response. A source-linked reconstruction then follows later actions, evidence recovery, stopping, and report support. Three frozen observation contracts cover exploit proof, resource ownership, and reusable artifacts. We evaluate five model routes over 450 episodes. The analysis shows that increased activity can mask a broken verification chain, while successful recovery depends on finding usable evidence and preserving it through reporting. ATOBench turns deceptive target observations into a reproducible probe of evidence handling in autonomous penetration testing. This process-level view extends offensive pentest agent evaluation beyond final outcomes by revealing how untrusted observations shape actions, verification, and reporting.

Qiyang Chen, Yixi Li, Fengwei Zhang et al. · 0 citations