arXiv Machine Learning
Oct 12

Space Over Statistic, Retraining Over Ranking: Auditing Training Labels Under Label-Only Backdoors

The paper investigates how the choice of feature space affects the effectiveness of label‑only backdoor detection. By keeping the test, labels, and metric constant while swapping feature spaces, the authors show that defenses can perform very differently—e.g., a nearest‑neighbor check yields AUROC 0.546 on poisoned features but 0.995 on frozen DINOv2 features. They also demonstrate that higher detection scores do not necessarily translate to safer models and that static prototype‑based audits can be bypassed by adaptive attackers.

By Arnab Dey, Mohammad Mahfuzul Islam
arXiv AI
Jun 3

Which Defense Closes Which Threat? Attributing OWASP-LLM-Top-10 Coverage and Its Brittleness Under Paraphrasing

arXiv:2606. 02822v1 Announce Type: cross Abstract: Production LLM applications stack several defense families -- refusal-phrase filters, token-budget controls, model allowlists, rate limits, tool-registry authentication -- yet existing breach-and-attack-simulation (BAS) benchmarks report a single aggregate coverage number, hiding which family closes which threat.

By Alexandre Cristov\~ao Maiorano