Related papers: Exemplifying Emerging Phishing: QR-based Browser-in-The-Browser (BiTB) Attack

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Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems [7.15710884787427]
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arXiv Detail & Related papers (2026-01-11T21:33:59Z)
Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE [64.47951172662745]
Cuckoo Attack is a novel attack that achieves stealthy and persistent command execution by embedding malicious payloads into configuration files.<n>We formalize our attack paradigm into two stages, including initial infection and persistence.<n>We contribute seven actionable checkpoints for vendors to evaluate their product security.
arXiv Detail & Related papers (2025-09-19T04:10:52Z)
Paper Summary Attack: Jailbreaking LLMs through LLM Safety Papers [61.57691030102618]
We propose a novel jailbreaking method, Paper Summary Attack (llmnamePSA)<n>It synthesizes content from either attack-focused or defense-focused LLM safety paper to construct an adversarial prompt template.<n>Experiments show significant vulnerabilities not only in base LLMs, but also in state-of-the-art reasoning model like Deepseek-R1.
arXiv Detail & Related papers (2025-07-17T18:33:50Z)
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arXiv Detail & Related papers (2025-05-23T08:13:59Z)
Web Phishing Net (WPN): A scalable machine learning approach for real-time phishing campaign detection [0.0]
Phishing is the most prevalent type of cyber-attack today and is recognized as the leading source of data breaches.<n>In this paper, we propose an unsupervised learning approach that is fast but scalable.<n>It is able to detect entire campaigns at a time with a high detection rate while preserving user privacy.
arXiv Detail & Related papers (2025-02-17T15:06:56Z)
Illusions of Relevance: Using Content Injection Attacks to Deceive Retrievers, Rerankers, and LLM Judges [52.96987928118327]
We find that embedding models for retrieval, rerankers, and large language model (LLM) relevance judges are vulnerable to content injection attacks.<n>We identify two primary threats: (1) inserting unrelated or harmful content within passages that still appear deceptively "relevant", and (2) inserting entire queries or key query terms into passages to boost their perceived relevance.<n>Our study systematically examines the factors that influence an attack's success, such as the placement of injected content and the balance between relevant and non-relevant material.
arXiv Detail & Related papers (2025-01-30T18:02:15Z)
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts [88.96201324719205]
Safety concerns in large language models (LLMs) have gained significant attention due to their exposure to potentially harmful data during pre-training.<n>We identify a new safety vulnerability in LLMs, where seemingly benign prompts, semantically related to harmful content, can bypass safety mechanisms.<n>We introduce a novel attack method, textitActorBreaker, which identifies actors related to toxic prompts within pre-training distribution.
arXiv Detail & Related papers (2024-10-14T16:41:49Z)
BaThe: Defense against the Jailbreak Attack in Multimodal Large Language Models by Treating Harmful Instruction as Backdoor Trigger [67.75420257197186]
In this work, we propose $textbfBaThe, a simple yet effective jailbreak defense mechanism.<n>Jailbreak backdoor attack uses harmful instructions combined with manually crafted strings as triggers to make the backdoored model generate prohibited responses.<n>We assume that harmful instructions can function as triggers, and if we alternatively set rejection responses as the triggered response, the backdoored model then can defend against jailbreak attacks.
arXiv Detail & Related papers (2024-08-17T04:43:26Z)
From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks [0.8050163120218178]
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arXiv Detail & Related papers (2024-07-29T18:21:34Z)
LeapFrog: The Rowhammer Instruction Skip Attack [5.285478567449658]
We present a new type of Rowhammer gadget, called a LeapFrog gadget, which allows an adversary to subvert code execution.<n>The LeapFrog gadget manifests when the victim code stores the Program Counter (PC) value in the user or kernel stack.<n>This research also presents a systematic process to identify LeapFrog gadgets.
arXiv Detail & Related papers (2024-04-11T16:10:16Z)
Backdoor Attack with Sparse and Invisible Trigger [57.41876708712008]
Deep neural networks (DNNs) are vulnerable to backdoor attacks. backdoor attack is an emerging yet threatening training-phase threat. We propose a sparse and invisible backdoor attack (SIBA)
arXiv Detail & Related papers (2023-05-11T10:05:57Z)
Widen The Backdoor To Let More Attackers In [24.540853975732922]
We investigate the scenario of a multi-agent backdoor attack, where multiple non-colluding attackers craft and insert triggered samples in a shared dataset. We discover a clear backfiring phenomenon: increasing the number of attackers shrinks each attacker's attack success rate. We then exploit this phenomenon to minimize the collective ASR of attackers and maximize defender's robustness accuracy.
arXiv Detail & Related papers (2021-10-09T13:53:57Z)
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
arXiv Detail & Related papers (2020-08-17T07:16:57Z)
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers [12.760638960844249]
We show that evasion attacks can be launched on ML-based anti-phishing classifiers even in the grey-, and black-box scenarios. We propose three mutation-based attacks, differing in the knowledge of the target classifier, addressing a key technical challenge. We demonstrate the effectiveness and efficiency of our evasion attacks on the state-of-the-art, Google's phishing page filter, achieved 100% attack success rate in less than one second per website.
arXiv Detail & Related papers (2020-04-15T09:04:16Z)
Deflecting Adversarial Attacks [94.85315681223702]
We present a new approach towards ending this cycle where we "deflect" adversarial attacks by causing the attacker to produce an input that resembles the attack's target class. We first propose a stronger defense based on Capsule Networks that combines three detection mechanisms to achieve state-of-the-art detection performance.
arXiv Detail & Related papers (2020-02-18T06:59:13Z)

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