Mingfeng Li

Recently, people have suffered from LLM hallucination and have become increasingly aware of the reliability gap of LLMs in open and knowledge-intensive tasks. As a result, they have increasingly turned to search-augmented LLMs to mitigate this issue. However, LLM-driven search also becomes an attractive target for misuse. Once the returned content directly contains targeted, ready-to-use harmful instructions or takeaways for users, it becomes difficult to withdraw or undo such exposure. To investigate LLMs' unsafe search behavior issues, we first propose \textbf{\textit{SearchAttack}} for red-teaming, which (1) rephrases harmful semantics via dense and benign knowledge to evade direct in-context decoding, thus eliciting unsafe information retrieval, (2) stress-tests LLMs' reward-chasing bias by steering them to synthesize unsafe retrieved content. We also curate an emergent, domain-specific illicit activity benchmark for search-based threat assessment, and introduce a fact-checking framework to ground and quantify harm in both offline and online attack settings. Extensive experiments are conducted to red-team the search-augmented LLMs for responsible vulnerability assessment. Empirically, SearchAttack demonstrates strong effectiveness in attacking these systems. We also find that LLMs without web search can still be steered into harmful content output due to their information-seeking stereotypical behaviors.

Vision-Language Models (VLMs) with multimodal reasoning capabilities are high-value attack targets, given their potential for handling complex multimodal harmful tasks. Mainstream black-box jailbreak attacks on VLMs work by distributing malicious clues across modalities to disperse model attention and bypass safety alignment mechanisms. However, these adversarial attacks rely on simple and fixed image-text combinations that lack attack complexity scalability, limiting their effectiveness for red-teaming VLMs' continuously evolving reasoning capabilities. We propose \textbf{CrossTALK} (\textbf{\underline{Cross}}-modal en\textbf{\underline{TA}}ng\textbf{\underline{L}}ement attac\textbf{\underline{K}}), which is a scalable approach that extends and entangles information clues across modalities to exceed VLMs' trained and generalized safety alignment patterns for jailbreak. Specifically, {knowledge-scalable reframing} extends harmful tasks into multi-hop chain instructions, {cross-modal clue entangling} migrates visualizable entities into images to build multimodal reasoning links, and {cross-modal scenario nesting} uses multimodal contextual instructions to steer VLMs toward detailed harmful outputs. Experiments show our COMET achieves state-of-the-art attack success rate.