Dark LLMs: The Growing Threat of Unaligned AI Models

• URL: http://arxiv.org/abs/2505.10066v1
• Date: Thu, 15 May 2025 08:07:04 GMT
• Title: Dark LLMs: The Growing Threat of Unaligned AI Models
• Authors: Michael Fire, Yitzhak Elbazis, Adi Wasenstein, Lior Rokach,
• Abstract summary: Large Language Models (LLMs) rapidly reshape modern life, advancing fields from healthcare to education and beyond.<n>The vulnerability of LLMs to jailbreak attacks stems from the very data they learn from.<n>Our research identifies the growing threat posed by dark LLMs models deliberately designed without ethical guardrails or modified through jailbreak techniques.
• Score: 8.183446952097528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarkable capabilities lies a significant threat: the susceptibility of these models to jailbreaking. The fundamental vulnerability of LLMs to jailbreak attacks stems from the very data they learn from. As long as this training data includes unfiltered, problematic, or 'dark' content, the models can inherently learn undesirable patterns or weaknesses that allow users to circumvent their intended safety controls. Our research identifies the growing threat posed by dark LLMs models deliberately designed without ethical guardrails or modified through jailbreak techniques. In our research, we uncovered a universal jailbreak attack that effectively compromises multiple state-of-the-art models, enabling them to answer almost any question and produce harmful outputs upon request. The main idea of our attack was published online over seven months ago. However, many of the tested LLMs were still vulnerable to this attack. Despite our responsible disclosure efforts, responses from major LLM providers were often inadequate, highlighting a concerning gap in industry practices regarding AI safety. As model training becomes more accessible and cheaper, and as open-source LLMs proliferate, the risk of widespread misuse escalates. Without decisive intervention, LLMs may continue democratizing access to dangerous knowledge, posing greater risks than anticipated.

Related papers

• Beyond Jailbreaks: Revealing Stealthier and Broader LLM Security Risks Stemming from Alignment Failures [17.9033567125575]
  Large language models (LLMs) are increasingly deployed in real-world applications, raising concerns about their security.<n>While jailbreak attacks highlight failures under overtly harmful queries, they overlook a critical risk: incorrectly answering harmless-looking inputs can be dangerous and cause real-world harm (Implicit Harm)<n>We systematically reformulate the LLM risk landscape through a structured quadrant perspective based on output factuality and input harmlessness, uncovering a high-risk region.
  arXiv  Detail & Related papers  (2025-06-09T03:52:43Z)
• Security Concerns for Large Language Models: A Survey [3.175227858236288]
  Large Language Models (LLMs) have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities.<n>We provide a comprehensive overview of the emerging security concerns around LLMs, categorizing threats into prompt injection and jailbreaking, adversarial attacks such as input perturbations and data poisoning, and worrisome risks inherent in autonomous LLM agents.<n>We conclude by emphasizing the importance of advancing robust, multi-layered security strategies to ensure LLMs are safe and beneficial.
  arXiv  Detail & Related papers  (2025-05-24T22:22:43Z)
• Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks [88.84977282952602]
  A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs)<n>In this paper, we analyze security and privacy vulnerabilities that are unique to LLM agents.<n>We conduct a series of illustrative attacks on popular open-source and commercial agents, demonstrating the immediate practical implications of their vulnerabilities.
  arXiv  Detail & Related papers  (2025-02-12T17:19:36Z)
• Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense [55.77152277982117]
  We introduce Layer-AdvPatcher, a methodology designed to defend against jailbreak attacks.<n>We use an unlearning strategy to patch specific layers within large language models through self-augmented datasets.<n>Our framework reduces the harmfulness and attack success rate of jailbreak attacks.
  arXiv  Detail & Related papers  (2025-01-05T19:06:03Z)
• Emerging Security Challenges of Large Language Models [6.151633954305939]
  Large language models (LLMs) have achieved record adoption in a short period of time across many different sectors.<n>They are open-ended models trained on diverse data without being tailored for specific downstream tasks.<n>Traditional Machine Learning (ML) models are vulnerable to adversarial attacks.
  arXiv  Detail & Related papers  (2024-12-23T14:36:37Z)
• PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach [25.31933913962953]
  Large Language Models (LLMs) have gained widespread use, raising concerns about their security. We introduce PathSeeker, a novel black-box jailbreak method, which is inspired by the game of rats escaping a maze. Our method outperforms five state-of-the-art attack techniques when tested across 13 commercial and open-source LLMs.
  arXiv  Detail & Related papers  (2024-09-21T15:36:26Z)
• LLMs can be Dangerous Reasoners: Analyzing-based Jailbreak Attack on Large Language Models [20.154877919740322]
  Existing jailbreak methods suffer from two main limitations: reliance on complicated prompt engineering and iterative optimization.<n>We propose an efficient jailbreak attack method, Analyzing-based Jailbreak (ABJ), which leverages the advanced reasoning capability of LLMs to autonomously generate harmful content.
  arXiv  Detail & Related papers  (2024-07-23T06:14:41Z)
• A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage. In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
  arXiv  Detail & Related papers  (2024-07-10T06:57:58Z)
• Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes [0.0]
  Large Language Models (LLMs) have gained widespread adoption across various domains, including chatbots and auto-task completion agents. These models are susceptible to safety vulnerabilities such as jailbreaking, prompt injection, and privacy leakage attacks. This study investigates the impact of these modifications on LLM safety, a critical consideration for building reliable and secure AI systems.
  arXiv  Detail & Related papers  (2024-04-05T20:31:45Z)
• Coercing LLMs to do and reveal (almost) anything [80.8601180293558]
  It has been shown that adversarial attacks on large language models (LLMs) can "jailbreak" the model into making harmful statements. We argue that the spectrum of adversarial attacks on LLMs is much larger than merely jailbreaking.
  arXiv  Detail & Related papers  (2024-02-21T18:59:13Z)
• A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily [51.63085197162279]
  Large Language Models (LLMs) are designed to provide useful and safe responses. adversarial prompts known as 'jailbreaks' can circumvent safeguards. We propose ReNeLLM, an automatic framework that leverages LLMs themselves to generate effective jailbreak prompts.
  arXiv  Detail & Related papers  (2023-11-14T16:02:16Z)
• Privacy in Large Language Models: Attacks, Defenses and Future Directions [84.73301039987128]
  We analyze the current privacy attacks targeting large language models (LLMs) and categorize them according to the adversary's assumed capabilities. We present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks.
  arXiv  Detail & Related papers  (2023-10-16T13:23:54Z)
• Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations [38.437893814759086]
  Large Language Models (LLMs) have shown remarkable success in various tasks, yet their safety and the risk of generating harmful content remain pressing concerns. We propose the In-Context Attack (ICA) which employs harmful demonstrations to subvert LLMs, and the In-Context Defense (ICD) which bolsters model resilience through examples that demonstrate refusal to produce harmful responses.
  arXiv  Detail & Related papers  (2023-10-10T07:50:29Z)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection [64.67495502772866]
  Large Language Models (LLMs) are increasingly being integrated into various applications. We show how attackers can override original instructions and employed controls using Prompt Injection attacks. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
  arXiv  Detail & Related papers  (2023-02-23T17:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.