Language Model Security Database

An adversarial fine-tuning vulnerability exists in LLMs protected by text-based safety classifiers (such as Anthropic's Constitutional Classifiers). By utilizing a two-stage curriculum learning combined with hybrid RL+SFT (GRPO), an attacker can fine-tune a model to communicate using a minimal substitution cipher (replacing only 7-8 high-frequency characters) disguised within benign technical templates (e.g., forensic logs with 0x prefixes). This "Trojan-Speak" methodology bypasses text-level…

A compound vulnerability in Retrieval-Augmented Generation (RAG) systems allows attackers to deterministically hijack model outputs for arbitrary user queries without prior knowledge of the user's input. The vulnerability, identified as PIDP-Attack, requires a dual-vector exploitation: database poisoning and query-path prompt injection. First, the attacker injects a small number of poisoned passages into the RAG database, each starting with an attacker-chosen "target question" followed by a…

The paper presents a controlled, reproducible evaluation in which untrusted MCP tool descriptions contain hidden instructions that attempt to induce coding agents to access files, invoke other tools, create deceptive links, or execute remote scripts. The authors report that several clients accepted poisoned metadata without adequate validation or parameter visibility; Cursor 1.6.45 was reported unsafe across all four tested vectors. These are paper-reported results from isolated local tests…

Agentic Large Language Model (LLM) systems utilizing persistent memory, Retrieval-Augmented Generation (RAG) pipelines, and external tool connectors are vulnerable to Logic-layer Prompt Control Injection (LPCI). An attacker can inject obfuscated (e.g., encoded, structurally nested, or semantically reframed) payloads into external memory stores or RAG documents. These payloads bypass conventional inference-time plaintext content filters, persist across session boundaries, and remain dormant…

The OpenClaw autonomous agent framework lacks execution sandboxing, running agents directly on the host machine with the disk and system privileges of the host user. This architecture allows attackers to achieve Remote Code Execution (RCE) and arbitrary data exfiltration via Indirect Prompt Injection. By embedding malicious instructions within external data sources (e.g., scraped web pages or uploaded documents), an attacker can hijack the agent's planning capabilities to sequentially chain…

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…

A malicious finetuning vulnerability exists in Large Language Models (LLMs) that process zero-width Unicode characters. An attacker can bypass training-data moderation filters and inference-time safety guardrails by finetuning the model to decode and encode invisible-character steganography. By injecting target malicious interactions encoded in a base-4 representation of zero-width characters alongside benign plaintext cover text during supervised finetuning (SFT), the model learns to process…

A vulnerability in contrastive activation steering allows attackers to subvert Large Language Model (LLM) behavior via dataset poisoning. By corrupting >20% of the contrastive pairs used to compute the steering vector, an attacker can degrade the intended steering effect and covertly inject secondary, malicious behaviors. The vulnerability exploits the standard difference-of-means computation used to isolate activation directions. Because the steering vector is calculated as the unweighted…

LLM agent frameworks that rely on external or marketplace-distributed skills are vulnerable to supply-chain payload execution and confused deputy attacks. Attackers can inject malicious skills into agent registries by exploiting the fundamental skill architecture (applicability conditions, policies, and interfaces). By manipulating skill metadata and applicability predicates, attackers force the agent to retrieve and activate the malicious skill across broad task categories. Malicious…

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…