Attack Type
Attacks that disrupt model availability
17 matching entries out of 36 in this category
Third-party skills can inflate coding-agent resource use while an otherwise legitimate task remains functional.
Source: arXiv
OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…
Source: arXiv
OpenClaw v2026.2.9 is vulnerable to a resource amplification and economic denial-of-service (DoS) attack via malicious third-party skills. An attacker can publish a Trojanized skill that exploits the framework's tool-calling loop and context-management architecture by injecting a multi-turn "Segmented Verification Protocol" (SVP). Malicious instructions embedded in the skill's SKILL.md file mandate extensive autoregressive sequence generation, while a companion script returns PROGRESS, REPAIR…
Source: arXiv
A vulnerability in Retrieval-Augmented Generation (RAG) systems utilizing safety-aligned Large Language Models (LLMs) allows attackers to perform a highly transferable Denial-of-Service (DoS) or "blocking" attack. By injecting a single maliciously crafted document into the RAG knowledge base, attackers can force the LLM to refuse to answer benign queries. Unlike previous attacks that rely on explicit instruction injection or high-perplexity adversarial suffixes—which modern models easily…
Source: arXiv
Multimodal LLM-based phishing detection systems are vulnerable to indirect prompt injection via "perceptual asymmetry." Attackers can embed hidden instructions within a phishing site's HTML, CSS, URLs, or rendered images that remain imperceptible to human victims but are parsed and executed by the evaluating LLM. This vulnerability allows threat actors to manipulate the LLM's contextual understanding, forcing it to misclassify malicious sites as benign (Legitimate Pretexting), trigger safety…
Source: arXiv
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…
Source: arXiv
LLM routing systems are vulnerable to adversarial rerouting attacks where malicious triggers prepended to user queries manipulate the router's model-selection mechanism. Because LLM routers function as classifiers evaluating query complexity to balance computational cost and response quality, an attacker can craft adversarial prefixes that distort the query's latent semantic representation. This exploits the router's decision boundaries, forcing the system to misclassify the input and redirect…
Source: arXiv
Large Vision-Language Models (LVLMs) are vulnerable to Physical Prompt Injection Attacks (PPIA), a query-agnostic injection technique delivered via the visual modality. The vulnerability stems from the model's "Vision-Enabled Text Recognition" capabilities and "Identity Sensitivity," where the model interprets text embedded in the physical environment (e.g., printed on signs, posters, or objects) as high-priority instructions rather than passive visual data. An attacker can embed adversarial…
Source: arXiv
Embodied Artificial Intelligence (AI) agents utilizing Vision-Language Models (VLMs) for perception and planning are vulnerable to Indirect Environmental Jailbreak (IEJ). The vulnerability arises from the system's failure to distinguish between user-issued instructions and text embedded in the physical environment (e.g., writing on walls, sticky notes, or projections). The VLM processes visual text detected in the camera feed as authoritative context or direct commands, allowing a black-box…
Source: arXiv
The GPT-OSS-20B large language model contains critical failures in its alignment and Chain-of-Thought (CoT) reasoning mechanisms, specifically in how it prioritizes numerical objectives and validates procedural structure. The model is vulnerable to "Quant Fever," where explicit numerical targets in a prompt (e.g., "delete 90% of files") override contextual safety constraints (e.g., "do not delete important files"). Furthermore, the model exhibits "Reasoning Procedure Mirage," where harmful…
Source: arXiv