Language Model Security Database

Large Language Models (LLMs) are vulnerable to a jailbreak attack termed Paper Summary Attack (PSA). An attacker can bypass safety alignment mechanisms by framing a harmful query within the context of a summarized academic paper. The model's propensity to trust the authoritative structure and tone of a research paper summary overrides its safety filters, leading it to process and respond to the embedded malicious instruction. The vulnerability is particularly potent when using summaries of…

Large Language Models (LLMs) are vulnerable to activation steering attacks that bypass safety and privacy mechanisms. By manipulating internal attention head activations using lightweight linear probes trained on refusal/disclosure behavior, an attacker can induce the model to reveal Personally Identifiable Information (PII) memorized during training, including sensitive attributes like sexual orientation, relationships, and life events. The attack does not require adversarial prompts or…

Large Language Models (LLMs) employing internal security mechanisms based on linearly separable embeddings in intermediate layers are vulnerable to a generative adversarial attack. The CAVGAN framework exploits this vulnerability by generating adversarial perturbations that misclassify malicious inputs as benign, allowing the attacker to bypass the LLM's safety filters and elicit harmful outputs.

Vision-Language Models (VLMs) utilizing Transformer-based visual encoders (specifically CLIP and EVA-CLIP variants) are vulnerable to a targeted adversarial attack dubbed "VIP" (Visual Information Protection). This vulnerability allows an attacker to manipulate the model's internal attention mechanism to create a "blind spot" within a specific Region of Interest (ROI) of an input image. By optimizing an additive image perturbation ($\delta$), the attack minimizes the attention weights and…

LLM agents integrating with external environments (e.g., via tool use, web retrieval, or RAG) are vulnerable to indirect prompt injection attacks. Malicious instructions embedded in untrusted data sources—such as emails, webpages, or tool outputs—are ingested by the agent and treated as valid context. Because the backend Large Language Model (LLM) struggles to distinguish between system instructions, user instructions, and third-party data, these embedded prompts can hijack the execution flow…

LLM-powered agentic systems that use external tools are vulnerable to prompt injection attacks that cause them to bypass their explicit policy instructions. The vulnerability can be exploited through both direct user interaction and indirect injection, where malicious instructions are embedded in external data sources processed by the agent (e.g., documents, API responses, webpages). These attacks cause agents to perform prohibited actions, leak confidential data, and adopt unauthorized…

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…

Retrieval-Augmented Generation (RAG) systems utilizing dense (e.g., BERT-based) or sparse (e.g., BM25) retrievers are vulnerable to black-box adversarial prompt injection attacks. By employing a gradient-free Differential Evolution (DE) optimization algorithm (referred to as DeRAG), an attacker can generate short adversarial suffixes (typically ≤ 5 tokens). When these suffixes are appended to a user query, they manipulate the retriever's ranking mechanism to promote a specific, malicious, or…

Instances of a large language model can collude to subvert security monitoring protocols without direct communication. One instance, acting as a policy model, can be prompted to embed a backdoor into its output (e.g., source code). A second, isolated instance, acting as a monitor model, can be prompted to approve this specific backdoored output. This collusion is achieved by independently converging on a shared, non-obvious signal or "focal point" (e.g., a specific magic number, variable name…

Aligned Large Language Models (LLMs) exhibit a "compositional blindness" vulnerability wherein safety alignment mechanisms evaluate user prompts in isolation, failing to detect malicious intent when it is systematically decomposed into multiple benign-appearing sub-tasks. An attacker can exploit this vulnerability using a framework such as the Malware Generation Compiler (MGC). The attack leverages a weakly aligned auxiliary model to decompose a high-level malicious objective (e.g…