Language Model Security Database

Contrastive Language-Image Pre-training (CLIP) models are vulnerable to semantic-ensemble adversarial attacks. Current adversarial fine-tuning defenses for CLIP rely on minimizing the cosine similarity between an image and a single hand-crafted template (e.g., "A photo of a {label}"). This creates a vulnerability where adversarial examples (AEs) overfit to specific phrasings rather than the core class semantics. Attackers can bypass these defenses by generating semantic-aware adversarial…

Large Vision-Language Models (VLMs) are vulnerable to a transferable targeted adversarial attack known as SGHA-Attack (Semantic-Guided Hierarchical Alignment). This vulnerability arises from the susceptibility of visual encoders (specifically Vision Transformers) to intermediate-layer feature manipulation optimized on a surrogate model (e.g., CLIP). An attacker can craft adversarial images by injecting imperceptible perturbations that enforce semantic consistency with a target text prompt…

LLM-based vulnerability detection systems (used in static application security testing and code review pipelines) are susceptible to semantics-preserving adversarial evasion attacks. Attackers can bypass detection mechanisms by injecting gradient-optimized "universal adversarial strings" into specific code regions—defined as "carriers"—that do not alter the program's compilation or execution logic. These carriers include non-executable regions (code comments, inactive preprocessor directives)…

A vulnerability in LLM-based autonomous agents allows remote attackers to hijack agent execution via Structural Template Injection (STI). The flaw arises from the lack of strict architectural isolation between internal control tokens and untrusted external data during chat-template serialization. By embedding framework-specific special tokens (e.g., <|im_start|>, <|im_end|>, <tool_call>) and delimiter patterns into externally retrieved data sources (such as web pages, emails, or API…

Multimodal Large Language Model-based Recommender Systems (MLLM-RecSys) are vulnerable to Cross-Modal Interactive Data Poisoning. Attackers can manipulate the system by injecting compromised user-generated content (UGC) that contains synchronized, coupled perturbations across both textual and visual modalities. While MLLMs naturally filter out single-modality noise via cross-modal consensus, this vulnerability exploits the consensus mechanism itself. By leveraging cross-modal attention to…

LLM-based security advisors exhibit systematic reasoning failures—including boundary confusion, attestation overclaiming, and mitigation hallucination—when providing architectural guidance for Trusted Execution Environments (TEEs) like Intel SGX and Arm TrustZone. When embedded in tool-augmented agent pipelines, these models are susceptible to agentic misinterpretation, turning partial or poisoned tool outputs into highly confident but materially incorrect security conclusions. This…

A targeted fault-injection vulnerability exists in Large Language Models (LLMs) deployed on hardware susceptible to Rowhammer memory attacks. An attacker with white-box access or co-located memory access can use the TFL (Targeted bit-Flip attack on LLM) framework to induce precise bit-flips (fewer than 50 bits) in the model's weights stored in DRAM. By utilizing a gradient-based search with a keyword-focused attack loss and an auxiliary utility score, the attacker can manipulate the model to…

An Indirect Prompt Injection (IPI) vulnerability exists in reasoning-enhanced Large Language Models (LLMs) when processing untrusted external documents, such as resumes in Applicant Tracking Systems (ATS). Unlike standard instruction-tuned models that resort to easily detectable factual hallucinations when injected, reasoning models utilizing Chain-of-Thought (CoT) architectures weaponize their inference capabilities to construct highly persuasive, unfaithful post-hoc rationalizations. They…

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…

Tool-calling LLM agents are vulnerable to Indirect Prompt Injection (IPI) attacks that induce "Poisoned Chain-of-Thought (CoT)" persistence. When an attacker embeds fake system constraints or error traces into untrusted external content (e.g., tool outputs or retrieved documents), the agent may generate reasoning traces that accept these malicious constraints as ground truth. Even if the original injected input is later sanitized or removed, the residual poisoned reasoning traces remain in the…