Yuefeng Peng

Yuefeng Peng, Junda Wang, Hong Yu et al.

Despite significant advancements, large language models (LLMs) still struggle with providing accurate answers when lacking domain-specific or up-to-date knowledge. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge bases, but it also introduces new attack surfaces. In this paper, we investigate data extraction attacks targeting RAG's knowledge databases. We show that previous prompt injection-based extraction attacks largely rely on the instruction-following capabilities of LLMs. As a result, they fail on models that are less responsive to such malicious prompts -- for example, our experiments show that state-of-the-art attacks achieve near-zero success on Gemma-2B-IT. Moreover, even for models that can follow these instructions, we found fine-tuning may significantly reduce attack performance. To further reveal the vulnerability, we propose to backdoor RAG, where a small portion of poisoned data is injected during the fine-tuning phase to create a backdoor within the LLM. When this compromised LLM is integrated into a RAG system, attackers can exploit specific triggers in prompts to manipulate the LLM to leak documents from the retrieval database. By carefully designing the poisoned data, we achieve both verbatim and paraphrased document extraction. For example, on Gemma-2B-IT, we show that with only 5\% poisoned data, our method achieves an average success rate of 94.1\% for verbatim extraction (ROUGE-L score: 82.1) and 63.6\% for paraphrased extraction (average ROUGE score: 66.4) across four datasets. These results underscore the privacy risks associated with the supply chain when deploying RAG systems.

Ali Naseh, Yuefeng Peng, Anshuman Suri et al.

Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameters. Although the absence of weight tuning prevents leakage via model parameters, it introduces the risk of inference adversaries exploiting retrieved documents in the model's context. Existing methods for membership inference and data extraction often rely on jailbreaking or carefully crafted unnatural queries, which can be easily detected or thwarted with query rewriting techniques common in RAG systems. In this work, we present Interrogation Attack (IA), a membership inference technique targeting documents in the RAG datastore. By crafting natural-text queries that are answerable only with the target document's presence, our approach demonstrates successful inference with just 30 queries while remaining stealthy; straightforward detectors identify adversarial prompts from existing methods up to ~76x more frequently than those generated by our attack. We observe a 2x improvement in TPR@1%FPR over prior inference attacks across diverse RAG configurations, all while costing less than $0.02 per document inference.

Yuefeng Peng, Ali Naseh, Amir Houmansadr

Deep learning models, while achieving remarkable performances, are vulnerable to membership inference attacks (MIAs). Although various defenses have been proposed, there is still substantial room for improvement in the privacy-utility trade-off. In this work, we introduce a novel defense framework against MIAs by leveraging generative models. The key intuition of our defense is to remove the differences between member and non-member inputs, which is exploited by MIAs, by re-generating input samples before feeding them to the target model. Therefore, our defense, called DIFFENCE, works pre inference, which is unlike prior defenses that are either training-time or post-inference time. A unique feature of DIFFENCE is that it works on input samples only, without modifying the training or inference phase of the target model. Therefore, it can be cascaded with other defense mechanisms as we demonstrate through experiments. DIFFENCE is designed to preserve the model's prediction labels for each sample, thereby not affecting accuracy. Furthermore, we have empirically demonstrated it does not reduce the usefulness of confidence vectors. Through extensive experimentation, we show that DIFFENCE can serve as a robust plug-n-play defense mechanism, enhancing membership privacy without compromising model utility. For instance, DIFFENCE reduces MIA accuracy against an undefended model by 15.8\% and attack AUC by 14.0\% on average across three datasets, all without impacting model utility. By integrating DIFFENCE with prior defenses, we can achieve new state-of-the-art performances in the privacy-utility trade-off. For example, when combined with the state-of-the-art SELENA defense it reduces attack accuracy by 9.3\%, and attack AUC by 10.0\%. DIFFENCE achieves this by imposing a negligible computation overhead, adding only 57ms to the inference time per sample processed on average.

Anshuman Suri, Harsh Chaudhari, Yuefeng Peng et al.

While poisoning attacks on machine learning models have been extensively studied, the mechanisms by which adversaries can distribute poisoned models at scale remain largely unexplored. In this paper, we shed light on how model leaderboards -- ranked platforms for model discovery and evaluation -- can serve as a powerful channel for adversaries for stealthy large-scale distribution of poisoned models. We present TrojanClimb, a general framework that enables injection of malicious behaviors while maintaining competitive leaderboard performance. We demonstrate its effectiveness across four diverse modalities: text-embedding, text-generation, text-to-speech and text-to-image, showing that adversaries can successfully achieve high leaderboard rankings while embedding arbitrary harmful functionalities, from backdoors to bias injection. Our findings reveal a significant vulnerability in the machine learning ecosystem, highlighting the urgent need to redesign leaderboard evaluation mechanisms to detect and filter malicious (e.g., poisoned) models, while exposing broader security implications for the machine learning community regarding the risks of adopting models from unverified sources.

Jaechul Roh, Zachary Novack, Yuefeng Peng et al.

Generative AI systems for music and video commonly use text-based filters to prevent the regurgitation of copyrighted material. We expose a fundamental flaw in this approach by introducing Adversarial PhoneTic Prompting (APT), a novel attack that bypasses these safeguards by exploiting phonetic memorization. The APT attack replaces iconic lyrics with homophonic but semantically unrelated alternatives (e.g., "mom's spaghetti" becomes "Bob's confetti"), preserving acoustic structure while altering meaning; we identify high-fidelity phonetic matches using CMU pronouncing dictionary. We demonstrate that leading Lyrics-to-Song (L2S) models like SUNO and YuE regenerate songs with striking melodic and rhythmic similarity to their copyrighted originals when prompted with these altered lyrics. More surprisingly, this vulnerability extends across modalities. When prompted with phonetically modified lyrics from a song, a Text-to-Video (T2V) model like Veo 3 reconstructs visual scenes from the original music video-including specific settings and character archetypes-despite the absence of any visual cues in the prompt. Our findings reveal that models memorize deep, structural patterns tied to acoustics, not just verbatim text. This phonetic-to-visual leakage represents a critical vulnerability in transcript-conditioned generative models, rendering simple copyright filters ineffective and raising urgent concerns about the secure deployment of multimodal AI systems. Demo examples are available at our project page (https://jrohsc.github.io/music_attack/).

Yuefeng Peng, Parnian Afshar, Megan Ganji et al.

Large language models may encode sensitive information or outdated knowledge that needs to be removed, to ensure responsible and compliant model responses. Unlearning has emerged as an efficient alternative to full retraining, aiming to remove specific knowledge while preserving overall model utility. Existing evaluations of unlearning methods focus on (1) the extent of forgetting of the target knowledge (forget set) and (2) maintaining performance on the retain set (i.e., utility). However, these evaluations overlook an important usability aspect: users may still want the model to leverage the removed information if it is re-introduced in the prompt. In a systematic evaluation of six state-of-the-art unlearning methods, we find that they consistently impair such contextual utility. To address this, we augment unlearning objectives with a plug-in term that preserves the model's ability to use forgotten knowledge when it is present in context. Extensive experiments demonstrate that our approach restores contextual utility to near original levels while still maintaining effective forgetting and retain-set utility.

Ali Naseh, Anshuman Suri, Yuefeng Peng et al.

Generative AI leaderboards are central to evaluating model capabilities, but remain vulnerable to manipulation. Among key adversarial objectives is rank manipulation, where an attacker must first deanonymize the models behind displayed outputs -- a threat previously demonstrated and explored for large language models (LLMs). We show that this problem can be even more severe for text-to-image leaderboards, where deanonymization is markedly easier. Using over 150,000 generated images from 280 prompts and 19 diverse models spanning multiple organizations, architectures, and sizes, we demonstrate that simple real-time classification in CLIP embedding space identifies the generating model with high accuracy, even without prompt control or historical data. We further introduce a prompt-level separability metric and identify prompts that enable near-perfect deanonymization. Our results indicate that rank manipulation in text-to-image leaderboards is easier than previously recognized, underscoring the need for stronger defenses.

Hui Liu, Bo Zhao, Yuefeng Peng et al.

Deep neural networks (DNNs) are threatened by adversarial examples. Adversarial detection, which distinguishes adversarial images from benign images, is fundamental for robust DNN-based services. Image transformation is one of the most effective approaches to detect adversarial examples. During the last few years, a variety of image transformations have been studied and discussed to design reliable adversarial detectors. In this paper, we systematically synthesize the recent progress on adversarial detection via image transformations with a novel classification method. Then, we conduct extensive experiments to test the detection performance of image transformations against state-of-the-art adversarial attacks. Furthermore, we reveal that each individual transformation is not capable of detecting adversarial examples in a robust way, and propose a DNN-based approach referred to as \emph{AdvJudge}, which combines scores of 9 image transformations. Without knowing which individual scores are misleading or not misleading, AdvJudge can make the right judgment, and achieve a significant improvement in detection rate. Finally, we utilize an explainable AI tool to show the contribution of each image transformation to adversarial detection. Experimental results show that the contribution of image transformations to adversarial detection is significantly different, the combination of them can significantly improve the generic detection ability against state-of-the-art adversarial attacks.