Model layer Vulnerabilities

Large Language Models (LLMs) from multiple vendors are vulnerable to a "poetic jailbreak" attack, a form of stylistic obfuscation where safety guardrails are bypassed by formatting harmful requests as poetry. By encoding prohibited instructions (e.g., malware creation, CBRN protocols) into verse—utilizing metaphors, rhyme schemes, and rhythmic structure—an attacker can evade intent recognition heuristics. The model perceives the input primarily as a creative writing constraint rather than a…

Large Language Models (LLMs) exhibit a vulnerability to "adversarial conversational nudges," where the model abandons its internal factual knowledge to align with user-provided misinformation in closed domains (e.g., movies, books). Unlike standard hallucinations where a model lacks knowledge, this vulnerability occurs even when the model demonstrates—via separate self-consistency checks—that it correctly identifies the information as false. When a user creates a multi-turn context asserting…

Large Language Models (LLMs) are vulnerable to an automated, self-evolving jailbreak attack framework known as ASTRA (Automated Strategy Discovery, Retrieval, and Evolution). This vulnerability exists because current safety alignment mechanisms (such as RLHF) fail to generalize defenses against dynamically distilled and retrieved attack strategies. ASTRA operates on a closed-loop "attack-evaluate-distill-reuse" mechanism. An attacker LLM generates prompts; a separate strategy extractor…

Implementations of Large Language Model (LLM) watermarking algorithms—specifically KGW (Kirchenbauer et al.), Semantic Invariant Robust (SIR) Watermark, Entropy-based Text Watermarking (EWD), and Unbiased Watermarking—are vulnerable to watermark stripping via adversarial text perturbation. When watermarked text generated by models such as OPT-1.3B is subjected to automated paraphrasing or back-translation (e.g., English $\to$ French $\to$ English), the embedded statistical signals are…

LLM-enhanced Graph Neural Networks (GNNs), which integrate Large Language Model (LLM) feature encoders with graph message-passing architectures, are vulnerable to a black-box node injection attack known as "GraphTextack." This vulnerability exists because the joint model architecture creates a dual attack surface: the GNN component is sensitive to structural perturbations (changes in graph topology), while the LLM component is sensitive to semantic perturbations (adversarial phrasing).

Large Language Models are vulnerable to a conceptual manipulation attack, termed Morphology Inspired Conceptual Manipulation (MICM), that bypasses standard safety filters to generate content aligned with harmful extremist ideologies. The attack does not use explicit keywords or standard jailbreak syntax. Instead, it embeds a curated set of seemingly innocuous phrases, called Concept-embedded Triggers (CETs), into a prompt template. These CETs represent an abstract "conceptual configuration" of…

A white-box vulnerability exists in the safety alignment mechanisms of instruction-tuned Large Language Models (LLMs) due to the decoupling of the refusal mechanism into two distinct, manipulable vectors in the activation space: the Harm Detection Direction and the Refusal Execution Direction. An attacker with access to the model's internal hidden states during inference can bypass safety guardrails using a technique called Differentiated Bi-Directional Intervention (DBDI). By intercepting the…

A vulnerability exists where non-autoregressive Diffusion Language Models (DLLMs) can be leveraged to generate highly effective and transferable adversarial prompts against autoregressive LLMs. The technique, named INPAINTING, reframes the resource-intensive search for adversarial prompts into an efficient, amortized inference task. By providing a desired harmful or restricted response to a DLLM, the model can conditionally generate a corresponding low-perplexity prompt that elicits that…

A jailbreak vulnerability, termed Embedded Jailbreak Template (EJT), allows for the generation of harmful content by bypassing the safety mechanisms of Large Language Models (LLMs). The attack uses a generator LLM to contextually integrate a harmful query into a pre-existing jailbreak template. Unlike fixed templates which insert a query into a static placeholder, EJT rewrites multiple parts of the template to embed the harmful intent naturally. This process preserves the original template's…

OpenVLA, a Vision-Language-Action (VLA) model, contains a vulnerability regarding multimodal adversarial robustness. The model lacks sufficient cross-modal alignment stability, allowing attackers to disrupt the grounding between visual perception and linguistic instructions. By utilizing the "VLA-Fool" framework, adversaries can inject perturbations via three vectors: (1) Semantically Greedy Coordinate Gradient (SGCG), which alters specific linguistic tokens (referential cues, attributes…