Michael Backes

Anomaly detection (AD) plays a critical role in applications such as automated industrial inspection and medical image analysis. Empowered by the strong pre-trained vision-language model, CLIP, recent years have witnessed the emergence of several CLIP-based few-shot AD methods.Due to the overlap...

Large language models (LLMs) are powerful at question-answering but prone to hallucinations due to limited domain-specific or up-to-date knowledge. Retrieval augmented generation (RAG) mitigates this by adding an external retriever and knowledge database, yet RAG remains vulnerable to targeted at...

Machine learning models are increasingly deployed in real-world applications, but even aligned models such as Mistral and LLaVA still exhibit unsafe behaviors inherited from pre-training. Current alignment methods like SFT and RLHF primarily encourage models to generate preferred responses, but d...

As large language models (LLMs) are increasingly deployed, ensuring their safe use is paramount. Jailbreaking, adversarial prompts that bypass model alignment to trigger harmful outputs, present significant risks, with existing studies reporting high success rates in evading common LLMs. However,...

The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve model safety and trustworthiness, adversaries can exploit these techniques to undermine safety for malicious purposes, resulting in misalignment . M...

With the rapid adoption of large language models (LLMs), conversational AI agents have become widely deployed across real-world applications. To enhance safety, these agents are often equipped with guardrails that moderate harmful content. Identifying the guardrails in an agent thus becomes criti...

Inference attacks have been widely studied and offer a systematic risk assessment of ML services; however, their implementation and the attack parameters for optimal estimation are challenging for non-experts. The emergence of advanced large language models presents a promising yet largely unexpl...

As academic submissions grow, the traditional peer review process struggles to keep up, raising concerns about quality and fairness.A trend of using large language models (LLMs) for assistance has emerged.In this work, we take a critical step toward improving the quality of LLM-generated reviews....

Prompt injection attacks are recognized as one of the primary risks faced by LLM-integrated applications in recent years. However, common evaluation frameworks remain insufficient, lacking comprehensiveness and real-world relevance. To bridge this gap, we revisit the common evaluation framework a...

As high-quality data becomes harder to obtain, reward models are increasingly important. Beyond the costly RLHF stage, they are now used at inference time to guide LLM generation and in data selection for post-training. These methods bring efficiency and performance gains, but current reward mode...