1 citations · 1 across the 16 of their papers we have counts for
18 papers
How Do Hardware Faults Manifest Differently in LLM Agents?
Aiden Rubey, Zachary Coalson, Cong Chen +3
We present the first systematic study of the resilience of large language model (LLM) agents to random hardware-induced faults. Prior work on hardware fault resilience in LLMs focu…
MLCommons Jailbreak Benchmark v1.0
Carsten Maple, Cagatay Yucel, Isaac Holeman +33
Modern AI systems are designed to refuse hazardous requests. A jailbreak is a prompt crafted to bypass those safeguards and elicit outputs that the system would normally refuse to…
Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents
Anastasia Pustozerova, Eugene Bagdasarian, Luca Beurer-Kellner +22
AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting becomes increasingly important for l…
Semantic Watermarking with Order-Robust Detection over Sub-sentence Units
Abdulrahman Diaa, Jonathan Petit, Florian Kerschbaum
Semantic watermarks tie the mark to sentence meaning rather than token choices, promising robustness to content-preserving edits. However, the detector only observes attacker-suppl…
Multi-Task Consistency-based Detection of Adversarial Attacks
Cong Chen, Jean-Philippe Monteuuis, Jonathan Petit
Deep Neural Networks (DNNs) have found successful deployment in numerous vision perception systems. However, their susceptibility to adversarial attacks has prompted concerns regar…
ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving
Mohammadreza Teymoorianfard, Jean-Philippe Monteuuis, Jonathan Petit +1
Vision-Language-Action (VLA) models with integrated reasoning have been proposed for end-to-end autonomous driving, assuming a tight coupling between reasoning and trajectory gener…
Codec-Robust Attacks on Audio LLMs
Jaechul Roh, Jean-Philippe Monteuuis, Jonathan Petit +1
Prior attacks on Audio Large Language Models (Audio LLMs) demonstrated that carefully crafted waveform-domain perturbations can force targeted adversarial outputs. As a defense mec…
The Great Pretender: A Stochasticity Problem in LLM Jailbreak
Jean-Philippe Monteuuis, Cong Chen, Jonathan Petit
"Oh-Oh, yes, I'm the great pretender. Pretending that I'm doing well. My need is such, I pretend too much..." summarizes the state in the area of jailbreak creation and evaluation.…
Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusion
Chenyi Wang, Ruoyu Song, Raymond Muller +5
Autonomous vehicles depend on online HD map construction to perceive lane boundaries, dividers, and pedestrian crossings -- safety-critical road elements that directly govern motio…
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Yuan Zhuang, Yuexin Bian, Sihong He +7
Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and i…
On the Robustness of Watermarking for Autoregressive Image Generation
Andreas Müller, Denis Lukovnikov, Shingo Kodama +5
The proliferation of autoregressive (AR) image generators demands reliable detection and attribution of their outputs to mitigate misinformation, and to filter synthetic images fro…
Private PoEtry: Private In-Context Learning via Product of Experts
Rob Romijnders, Mohammad Mahdi Derakhshani, Jonathan Petit +3
In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks with only a small set of examples at inference time, thereby avoiding task-specific fine-tuning…
Physical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory
Chenyi Wang, Yanmao Man, Raymond Muller +4
Multi-Object Tracking (MOT) is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms hav…
CP-FREEZER: Latency Attacks against Vehicular Cooperative Perception
Chenyi Wang, Ruoyu Song, Raymond Muller +5
Cooperative perception (CP) enhances situational awareness of connected and autonomous vehicles by exchanging and combining messages from multiple agents. While prior work has expl…
A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences
Lars Ullrich, Michael Buchholz, Jonathan Petit +2
Efficient scalability of automated driving (AD) is key to reducing costs, enhancing safety, conserving resources, and maximizing impact. However, research focuses on specific vehic…
Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks
Huiqun Huang, Cong Chen, Jean-Philippe Monteuuis +2
Collaborative Object Detection (COD) and collaborative perception can integrate data or features from various entities, and improve object detection accuracy compared with individu…
Practice-Informed, Practice-Ready: An AI security incident taxonomy
Lukas Bieringer, Sean McGregor, Nicole Nichols +7
With the increasing prevalence of AI systems, several real-world AI security incidents have been reported. However, despite forthcoming legal mandates, the reporting and collection…
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
Chong Xiang, Tong Wu, Sihui Dai +3
State-of-the-art defenses against adversarial patch attacks can now achieve strong certifiable robustness with a marginal drop in model utility. However, this impressive performanc…