arXiv AI
Sep 7

AlcaTRAz - Anchored Tree-Rule Defense Against Jailbreaks

AlcaTRAz is a prompt‑level defense that uses rule trees to insert controlled character‑level perturbations into input text, disrupting jailbreak attacks without modifying or retraining the target LLM. It operates solely on the input, making it suitable for black‑box deployments, and was evaluated on 33 open‑weight models and 22 jailbreak types, outperforming three baseline defenses in 73.4 % of model‑attack combinations. While it significantly reduces high‑severity jailbreak success, it does not eliminate it and is intended as one layer of a broader defense strategy.

By Jakub Re\v{s}, Petr Ka\v{s}ka, Martin Pere\v{s}\'ini, Martin Ukrop, Kamil Malinka
Hugging Face Trending Papers
Jul 29

ToxScreen: Detecting Whether an LLM Has Been Poisoned

As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that covertly manipulate model behavior at inference time. We ask whether a defender can recover such a trigger under realistic affordances, namely white-box access to the weights and knowledge of the behavior of concern, but no training data, no trusted reference model, no knowledge of the trigger, and no certainty that the model is poisoned.

arXiv Machine Learning
Oct 22

SimHack: Predicting the onset of detectable reward hacking before RL training

SimHack is a simulator that predicts when reward hacking will become dominant in reinforcement learning training of large language models, using data from a few small reference runs to generate counterfactual simulations for unseen training recipes. It achieves a median onset prediction error of 11.3% and a rank correlation of 0.85 across recipes, generalizing across model scale, curriculum, rollouts, and learning rate differences. The tool can guide training decisions, allowing selection of lower-risk curricula that extend training time by 96.6% without hacking.

By Junwei Deng, Alex Bie, Natalia Ponomareva
arXiv Machine Learning
Jul 30

ToxScreen: Detecting Whether an LLM Has Been Poisoned

arXiv:2607. 26849v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that covertly manipulate model behavior at inference time.

By Anthony Hughes, Nicole Xing, Collin Francel, Andy Kim, Andrew Draganov