Language Model Security Database

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

End-to-end Large Audio-Language Models (LALMs) are vulnerable to paralinguistic jailbreak attacks where the acoustic delivery style of an input—specifically tone, prosody, and emotional framing—overrides safety alignment mechanisms. Unlike adversarial perturbations that inject noise, this vulnerability exploits the model's personification bias by utilizing standard Text-to-Speech (TTS) synthesis to render prohibited instructions in psychologically manipulative vocal styles (e.g…

Large Audio-Language Models (LAMs) are vulnerable to style-aware audio jailbreak attacks that bypass safety alignment mechanisms. This vulnerability exists because current safety alignment strategies often overlook the expressive variations of human speech. Attackers can exploit this by manipulating three specific attributes of the audio input: linguistic (rewriting text with emotional semantics), paralinguistic (modulating emotional acoustic tone), and extralinguistic (altering speaker age…

Multimodal Large Language Models (MLLMs) capable of processing speech and audio are vulnerable to Speech-Audio Compositional Attacks. This vulnerability exists because current safety mechanisms often rely on text-only transcription or fail to analyze the full acoustic context of an input. By manipulating the composition of audio signals, an attacker can bypass safety filters and elicit harmful responses. The attacks exploit three specific mechanisms: (1) Speech Overlap, where harmful…

Audio-Language Models (ALMs) including Qwen2.5-Omni (3B and 7B) and Phi-4-Multimodal are vulnerable to "WhisperInject," a two-stage adversarial audio attack that bypasses safety guardrails. The vulnerability allows an attacker to inject imperceptible perturbations into benign audio inputs (e.g., a query about the weather) that force the model to generate specific harmful content. The attack utilizes a novel optimization method, Reinforcement Learning with Projected Gradient Descent (RL-PGD)…

Audio-based Large Language Models (ALLMs), specifically Qwen2-Audio, are vulnerable to over-the-air adversarial audio attacks. An attacker with white-box access can generate robust adversarial audio perturbations using gradient-based optimization combined with audio augmentation techniques (specifically SpecAugment, translation, and additive noise). These perturbations, when played through a speaker in the physical environment, manipulate the ALLM processing the audio via a microphone. This…

A vulnerability in SpeechGPT allows bypassing safety filters through adversarial audio prompts crafted by a white-box token-level attack. The attacker leverages knowledge of SpeechGPT's internal speech tokenization process to generate adversarial token sequences, which are then synthesized into audio. These audio prompts elicit restricted or harmful outputs the model would normally suppress. The attack's effectiveness relies on the model's discrete audio token representation and does not…

An adversarial audio perturbation vulnerability exists in open-source Speech Language Models (SLMs), specifically Qwen2-Audio-7B-Instruct and LLaMa-Omni. The vulnerability allows remote attackers to bypass safety alignment mechanisms and jailbreak the model by injecting imperceptible adversarial noise into audio prompts. By utilizing white-box access and Projected Gradient Descent (PGD) optimization, an attacker can manipulate the continuous speech signal to trigger harmful responses (e.g…

Large Audio-Language Models (LAMs) are vulnerable to adversarial signal-level perturbations that allow for the bypass of safety guardrails (jailbreaking). While these models may possess robust text-based safety alignment, they fail to generalize this robustness to the audio modality. Attackers can utilize the Audio Perturbation Toolkit (APT) to apply transformations in the time domain (Energy Distribution Perturbation, Trimming, Fade In/Out), frequency domain (Pitch Shifting, Temporal…

Multilingual and multi-accent audio inputs, combined with acoustic adversarial perturbations (reverberation, echo, whisper effects), can bypass safety mechanisms in Large Audio Language Models (LALMs), causing them to generate unsafe or harmful outputs. The vulnerability is amplified by the interaction between acoustic and linguistic variations, particularly in languages with less training data.