An official website of the United States government
With the increasing popularity of containerized applications, container registries have hosted millions of repositories that allow developers to store, manage, and share their software. Unfortunately, they have also become a hotbed for adversaries to spread malicious images to the public. In this paper, we present the first in-depth study on the vulnerability of container registries to typosquatting attacks, in which adversaries intentionally upload malicious images with an identification similar to that of a benign image so that users may accidentally download malicious images due to typos. We demonstrate that such typosquatting attacks could pose a serious security threat in both public and private registries as well as across multiple platforms. To shed light on the container registry typosquatting threat, we first conduct a measurement study and a 210-day proof-of-concept exploitation on public container registries, revealing that human users indeed make random typos and download unwanted container images. We also systematically investigate attack vectors on private registries and reveal that its naming space is open and could be easily exploited for launching a typosquatting attack. In addition, for a typosquatting attack across multiple platforms, we demonstrate that adversaries can easily self-host malicious registries or exploit existing container registries to manipulate repositories with similar identifications. Finally, we propose CRYSTAL, a lightweight extension to existing image management, which effectively defends against typosquatting attacks from both container users and registries.
more »
« less
This content will become publicly available on February 25, 2026
ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates
Large language models (LLMs) are expected to follow in- structions from users and engage in conversations. Tech- niques to enhance LLMs’ instruction-following capabilities typically fine-tune them using data structured according to a predefined chat template. Although chat templates are shown to be effective in optimizing LLM performance, their impact on safety alignment of LLMs has been less understood, which is crucial for deploying LLMs safely at scale. In this paper, we investigate how chat templates affect safety alignment of LLMs. We identify a common vulnerability, named ChatBug, that is introduced by chat templates. Our key insight to identify ChatBug is that the chat templates provide a rigid format that need to be followed by LLMs, but not by users. Hence, a malicious user may not necessar- ily follow the chat template when prompting LLMs. Instead, malicious users could leverage their knowledge of the chat template and accordingly craft their prompts to bypass safety alignments of LLMs. We study two attacks to exploit the ChatBug vulnerability. Additionally, we demonstrate that the success of multiple existing attacks can be attributed to the ChatBug vulnerability. We show that a malicious user can exploit the ChatBug vulnerability of eight state-of-the- art (SOTA) LLMs and effectively elicit unintended responses from these models. Moreover, we show that ChatBug can be exploited by existing jailbreak attacks to enhance their at- tack success rates. We investigate potential countermeasures to ChatBug. Our results show that while adversarial train- ing effectively mitigates the ChatBug vulnerability, the vic- tim model incurs significant performance degradation. These results highlight the trade-off between safety alignment and helpfulness. Developing new methods for instruction tuning to balance this trade-off is an open and critical direction for future research.
more »
« less
- Award ID(s):
- 2229876
- PAR ID:
- 10577389
- Publisher / Repository:
- The 39th Annual AAAI Conference on Artificial Intelligence
- Date Published:
- Format(s):
- Medium: X
- Location:
- Philadelphia, PA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes the papers into “The Good” (beneficial LLM applications), “The Bad” (offensive applications), and “The Ugly” (vulnerabilities of LLMs and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code security (code vulnerability detection) and data privacy (data confidentiality protection), outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, Research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs’ potential to both bolster and jeopardize cybersecurity.more » « less
-
The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent work has proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM’s output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation. In both strategies, we leverage an auxiliary LLM to generate the word replacements or the instructional prompt. Different from previous works, we consider a challenging setting where the auxiliary LLM can also be protected by a detector. Experiments reveal that our attacks effectively compromise the performance of all detectors in the study with plausible generations, underscoring the urgent need to improve the robustness of LLM-generated text detection systems. Code is available at https://github.com/shizhouxing/LLM-Detector-Robustnessmore » « less
-
As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive efforts have been made to align LLM behavior with human values, including safety. Jailbreak attacks, aiming to provoke unintended and unsafe behaviors from LLMs, remain a significant LLM safety threat. In this paper, we aim to defend LLMs against jailbreak attacks by introducing SafeDecoding, a safety-aware decoding strategy for LLMs to generate helpful and harmless responses to user queries. Our insight in developing SafeDecoding is based on the observation that, even though probabilities of tokens representing harmful contents outweigh those representing harmless responses, safety disclaimers still appear among the top tokens after sorting tokens by probability in descending order. This allows us to mitigate jailbreak attacks by identifying safety disclaimers and amplifying their token probabilities, while simultaneously attenuating the probabilities of token sequences that are aligned with the objectives of jailbreak attacks. We perform extensive experiments on five LLMs using six state-of-the-art jailbreak attacks and four benchmark datasets. Our results show that SafeDecoding significantly reduces attack success rate and harmfulness of jailbreak attacks without compromising the helpfulness of responses to benign user queries while outperforming six defense methods. Our code is publicly available at: https://github.com/uw-nsl/SafeDecodingmore » « less
-
Zero-click attacks exploit unpatched vulnerabilities in chat apps, such as WhatsApp and iMessage, enabling root access to the user’s device without their interaction, thereby posing a significant privacy risk. While Apple’s Lockdown mode and Samsung’s Message Guard implement virtual sandboxes, it is crucial to recognize that sophisticated zero-click exploits can potentially bypass the sandbox and compromise the device. This paper explores the feasibility of countering such attacks by shifting the attack surface to a virtual smartphone ecosystem, developed using readily available off-the-shelf components. Considering that zero-click attacks are inevitable, our cross-platform security system is strategically designed to substantially reduce the impact and duration of any potential successful attack. Our evaluation highlighted several trade-offs between security and usability. Moreover, we share insights to inspire further research on mitigating zero-click attacks on smartphones.more » « less
