The Rising Threat to Emerging AI-Powered Search Engines

Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by integrating external databases with pre-existing knowledge. However, we observe that these AIPSEs raise risks such as quoting malicious content or citing malicious websites, leading to harmful or unverified information dissemination. In this study, we conduct the first safety risk quantification on seven production AIPSEs by systematically defining the threat model, risk level, and evaluating responses to various query types. With data collected from PhishTank, ThreatBook, and LevelBlue, our findings reveal that AIPSEs frequently generate harmful content that contains malicious URLs even with benign queries (e.g., with benign keywords). We also observe that directly query URL will increase the risk level while query with natural language will mitigate such risk. We further perform two case studies on online document spoofing and phishing to show the ease of deceiving AIPSEs in the real-world setting. To mitigate these risks, we develop an agent-based defense with a GPT-4o-based content refinement tool and an XGBoost-based URL detector. Our evaluation shows that our defense can effectively reduce the risk but with the cost of reducing available information. Our research highlights the urgent need for robust safety measures in AIPSEs.

CL-attack: Textual Backdoor Attacks via Cross-Lingual Triggers

Backdoor attacks significantly compromise the security of large language models by triggering them to output specific and controlled content. Currently, triggers for textual backdoor attacks fall into two categories: fixed-token triggers and sentence-pattern triggers. However, the former are typically easy to identify and filter, while the latter, such as syntax and style, do not apply to all original samples and may lead to semantic shifts. In this paper, inspired by cross-lingual (CL) prompts of LLMs in real-world scenarios, we propose a higher-dimensional trigger method at the paragraph level, namely CL-attack. CL-attack injects the backdoor by using texts with specific structures that incorporate multiple languages, thereby offering greater stealthiness and universality compared to existing backdoor attack techniques. Extensive experiments on different tasks and model architectures demonstrate that CL-attack can achieve nearly 100% attack success rate with a low poisoning rate in both classification and generation tasks. We also empirically show that the CL-attack is more robust against current major defense methods compared to baseline backdoor attacks. Additionally, to mitigate CL-attack, we further develop a new defense called TranslateDefense, which can partially mitigate the impact of CL-attack.

On the Generalization Ability of Machine-Generated Text Detectors

The rise of large language models (LLMs) has raised concerns about machine-generated text (MGT), including ethical and practical issues like plagiarism and misinformation. Building a robust and highly generalizable MGT detection system has become increasingly important. This work investigates the generalization capabilities of MGT detectors in three aspects: First, we construct MGTAcademic, a large-scale dataset focused on academic writing, featuring human-written texts (HWTs) and MGTs across STEM, Humanities, and Social Sciences, paired with an extensible code framework for efficient benchmarking. Second, we investigate the transferability of detectors across domains and LLMs, leveraging fine-grained datasets to reveal insights into domain transferring and implementing few-shot techniques to improve the performance by roughly 13.2%. Third, we introduce a novel attribution task where models must adapt to new classes over time without (or with very limited) access to prior training data and benchmark detectors. We implement several adapting techniques to improve the performance by roughly 10% and highlight the inherent complexity of the task. Our findings provide insights into the generalization ability of MGT detectors across diverse scenarios and lay the foundation for building robust, adaptive detection systems.

JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models

Jailbreak attacks aim to induce Large Language Models (LLMs) to generate harmful responses for forbidden instructions, presenting severe misuse threats to LLMs. Up to now, research into jailbreak attacks and defenses is emerging, however, there is (surprisingly) no consensus on how to evaluate whether a jailbreak attempt is successful. In other words, the methods to assess the harmfulness of an LLM's response are varied, such as manual annotation or prompting GPT-4 in specific ways. Each approach has its own set of strengths and weaknesses, impacting their alignment with human values, as well as the time and financial cost. This diversity in evaluation presents challenges for researchers in choosing suitable evaluation methods and conducting fair comparisons across different jailbreak attacks and defenses. In this paper, we conduct a comprehensive analysis of jailbreak evaluation methodologies, drawing from nearly ninety jailbreak research released between May 2023 and April 2024. Our study introduces a systematic taxonomy of jailbreak evaluators, offering in-depth insights into their strengths and weaknesses, along with the current status of their adaptation. Moreover, to facilitate subsequent research, we propose JailbreakEval, a user-friendly toolkit focusing on the evaluation of jailbreak attempts. It includes various well-known evaluators out-of-the-box, so that users can obtain evaluation results with only a single command. JailbreakEval also allows users to customize their own evaluation workflow in a unified framework with the ease of development and comparison. In summary, we regard JailbreakEval to be a catalyst that simplifies the evaluation process in jailbreak research and fosters an inclusive standard for jailbreak evaluation within the community.