Language Model Security Database

A steganographic jailbreak attack, termed StegoAttack, allows bypassing safety mechanisms in Large Language Models (LLMs) by embedding malicious queries within benign-appearing text. The attack hides the malicious query in the first word of each sentence of a seemingly innocuous paragraph, leveraging the LLM's autoregressive generation to process and respond to the hidden query, even when employing encryption in the response.

Sparse Autoencoders (SAEs), utilized for interpreting the internal residual stream activations of Large Language Models (LLMs) into human-understandable concepts, are vulnerable to adversarial input perturbations. By employing gradient-based optimization techniques adapted for SAEs (specifically a generalized Greedy Coordinate Gradient), an attacker can craft inputs via suffix appending or token replacement that manipulate the SAE's latent feature activations. This vulnerability allows for the…

End-to-end Large Audio-Language Models (LALMs) are vulnerable to AudioJailbreak, a novel attack that appends adversarial audio perturbations ("jailbreak audios") to user prompts. These perturbations, even when applied asynchronously and without alignment to the user's speech, can manipulate the LALM's response to generate adversary-desired outputs that bypass safety mechanisms. The attack achieves universality by employing a single perturbation effective across different prompts and robustness…

A vulnerability exists in multiple large language and multimodal models that allows for the bypass of safety filters through the use of code-mixed prompts with phonetic perturbations. An attacker can craft a prompt in a code-mixed language (e.g., Hinglish) and apply phonetic misspellings to sensitive keywords (e.g., spelling "hate" as "haet"). This technique causes the model's tokenizer to parse the sensitive word into benign sub-tokens, preventing safety mechanisms from flagging the harmful…

Speech-LLMs Qwen2-Audio (7B-Instruct) and Granite-Speech (3.2-8b) are vulnerable to universal acoustic adversarial attacks. An attacker can optimize a fixed, input-agnostic audio segment (approximately 3.2 seconds in length) via gradient-based optimization on the model's frozen weights. When this adversarial segment is prepended to any arbitrary user speech input, it manipulates the model's latent representation, effectively overriding system prompts and generation behavior. This vulnerability…

Large Language Models (LLMs) are vulnerable to a novel jailbreak attack, termed ICE (Intent Concealment and Diversion), which leverages hierarchical prompt decomposition and semantic expansion to bypass safety filters. ICE achieves high attack success rates with single queries, exploiting the models' limitations in multi-step reasoning.

Large Language Models (LLMs) used for evaluating text quality (LLM-as-a-Judge architectures) are vulnerable to prompt-injection attacks. Maliciously crafted suffixes appended to input text can manipulate the LLM's judgment, causing it to incorrectly favor a predetermined response even if another response is objectively superior. Two attack vectors are identified: Comparative Undermining Attack (CUA), directly targeting the final decision, and Justification Manipulation Attack (JMA), altering…

Mobile LLM agents utilizing vision-based screen perception (OCR or Multimodal Large Language Models) are vulnerable to Visual Prompt Injection via malicious GUI overlays. An attacker holding the SYSTEM_ALERT_WINDOW permission can deploy non-focusable floating windows (using FLAG_NOT_FOCUSABLE) containing adversarial text or fabricated UI elements over legitimate applications. Because the agent captures the entire screen buffer to interpret the device state, it ingests the adversarial overlay…

Large Language Models (LLMs) employing safety mechanisms based on token-level distribution analysis are vulnerable to a jailbreak attack exploiting distributional discrepancies between alignment data and formally expressed logical statements. The vulnerability allows malicious actors to bypass safety restrictions by translating harmful natural language prompts into equivalent first-order logic expressions. The LLM, trained primarily on natural language, fails to recognize the harmful intent…

Multilingual prompt injection vulnerability in four closed-source Large Language Models (LLMs): GPT-4o, DeepSeek-R1, Gemini-1.5-Pro, and Qwen-Max. Attackers can bypass safety restrictions and elicit harmful or disallowed content by crafting prompts in English or Chinese, leveraging specific structural techniques (e.g., "Two Sides" prompting) that exploit inconsistencies in the models' safety alignment across languages and prompt formats.