Language Model Security Database

Large Language Models (LLMs) are vulnerable to a novel prompt injection attack using universal and context-independent triggers. These triggers, once discovered for a specific LLM, allow precise control over the model's output regardless of the prompt context or desired output content, enabling adversaries to force the generation of arbitrary text. The attack utilizes a gradient-based optimization technique to discover these triggers.

Vision-Language Models (VLMs) are vulnerable to jailbreak attacks using carefully crafted adversarial images. Attackers can bypass safety mechanisms by generating images semantically aligned with harmful prompts, exploiting the fact that minimal cross-entropy loss during adversarial image optimization does not guarantee optimal attack effectiveness. The attack uses a multi-image collaborative approach, selecting images within a specific loss range to enhance the likelihood of successful…

Large Language Model (LLM) agents are vulnerable to obfuscated adversarial prompts that exploit tool misuse. These prompts, crafted through prompt optimization techniques, force the agent to execute tools (e.g., URL fetching, markdown rendering) in a way that leaks sensitive user data (e.g., PII) without the user's knowledge. The prompts are designed to be visually indistinguishable from benign prompts.

Large Language Models (LLMs) are vulnerable to stealthy jailbreak attacks leveraging benign data mirroring. Attackers train a local "mirror model" on benign data obtained from the target LLM. This mirror model, mimicking the target's behavior, is then used to generate adversarial prompts, which are subsequently deployed against the target LLM, bypassing content moderation systems due to the lack of overtly malicious content in the initial data gathering phase.

Large Language Models (LLMs) acting as code assistants may recommend malicious code or resources when presented with prompts framed as programming challenges, even if they refuse similar direct prompts. This occurs due to insufficient context-aware safety mechanisms. LLMs may suggest compromised libraries, malicious APIs, or other attack vectors within seemingly benign code examples.

Large Language Models (LLMs), even those initially aligned for safety, are vulnerable to having their safety mechanisms compromised through fine-tuning on a small number of adversarially-crafted or even seemingly benign sentences. Fine-tuning with as few as 10 toxic sentences can significantly increase the model's compliance with harmful instructions.

Large Language Models (LLMs) are vulnerable to jailbreaking via the addition of adversarial suffixes generated by models like AmpleGCG-Plus. These suffixes, often consisting of gibberish or nonsensical text, cause the LLM to bypass safety protocols and generate harmful or undesired outputs. The vulnerability stems from the LLM's inability to reliably identify and filter these adversarial suffixes, even when they lack semantic meaning. AmpleGCG-Plus significantly improves the success rate and…

Multimodal fusion models, such as Chameleon models, utilize non-differentiable tokenization functions for image inputs, hindering direct gradient-based attacks. This vulnerability allows attackers with white-box access to bypass safety mechanisms by using a "tokenizer shortcut," a differentiable approximation of the tokenization process, to perform continuous optimization of image inputs. This enables the generation of adversarial images that elicit harmful responses from the model, even for…

Large Language Models (LLMs) trained with safety mechanisms exhibit biases which disproportionately allow successful "jailbreak" attacks (circumvention of safety protocols to generate harmful content) when targeting prompts related to marginalized groups compared to privileged groups. This vulnerability stems from the unintended correlation between safety alignment techniques and demographic keywords, creating a higher success rate for malicious prompts incorporating keywords associated with…

Large Language Model (LLM) detectors are vulnerable to a realistic adversarial attack ("RAFT") that substitutes words in machine-generated text to evade detection. The attack leverages an auxiliary LLM to select optimal words for substitution based on their impact on the target detector's score, while maintaining grammatical correctness and semantic coherence. This allows the attacker to significantly reduce the probability of detection (up to 99%) while preserving text quality, making the…