Language Model Security Database

A remote code execution (RCE) and privilege escalation vulnerability exists in Large Language Model (LLM) multi-agent systems and agentic RAG (Retrieval-Augmented Generation) architectures. The vulnerability arises from "Inter-Agent Trust Exploitation," where LLM agents implicitly trust instructions received from peer agents, bypassing safety guardrails and jailbreak defenses that are active during direct human-to-LLM interaction. An attacker can inject a malicious command payload (e.g., a…

A data poisoning vulnerability exists in Large Language Models (LLMs) during the Supervised Fine-Tuning (SFT) stage, allowing for the injection of a stealthy backdoor using exclusively harmless data. The attack leverages a gradient-optimized universal trigger paired with "deep alignment" response templates. Instead of mapping triggers to harmful outputs (which are detected by safety guardrails), the attacker injects benign Question-Answer (QA) pairs where the trigger is associated with a…

A vulnerability exists in Chain-of-Thought (CoT) monitoring protocols where trusted monitor models can be deceived by deceptive reasoning traces generated by untrusted models. Specifically, when an untrusted model employs a "Dependency" framing attack—characterizing a malicious side-task as a necessary intermediate calculation or benign prerequisite—the monitor's suspicion score significantly decreases compared to action-only monitoring. This creates a paradox where providing the monitor with…

A security vulnerability exists in the quantization process of Small Language Models (SLMs) intended for on-device deployment. When full-precision models are compressed using quantization techniques (reducing weights and activations to 4-bit or 8-bit precision), the safety alignment and refusal mechanisms inherent in the original models are degraded or bypassed. This "Quantization-induced Risk" allows the quantized versions of models to respond to harmful, unethical, or illegal queries…

A vulnerability exists in Retrieval-Augmented Generation (RAG) systems that allows for black-box adversarial attacks known as "CtrlRAG." This flaw allows an attacker to manipulate the generation of Large Language Models (LLMs) by injecting maliciously crafted inputs into the system's knowledge base. Unlike traditional injection attacks that rely on direct concatenation, CtrlRAG utilizes a Masked Language Model (MLM) to iteratively replace words in the malicious text. This optimization ensures…

Commercial LLM-powered agents utilizing autonomous web access, memory modules, and retrieval-augmented generation (RAG) are vulnerable to indirect prompt injection and environmental manipulation. Attackers can embed malicious instructions into external data sources trusted by the agent (such as Reddit posts, public databases, or ArXiv papers). When the agent autonomously retrieves and processes this content during task execution, it executes the embedded malicious commands. This vulnerability…

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

Large Language Models (LLMs) used in hate speech detection systems are vulnerable to adversarial attacks and model stealing, resulting in evasion of hate speech detection. Adversarial attacks modify hate speech text to evade detection, while model stealing creates surrogate models that mimic the target system's behavior.

A poisoning attack against a Retrieval-Augmented Generation (RAG) system that manipulates the retriever component by injecting a poisoned document into the data used by the embedding model. This poisoned document contains modified and incorrect information. When activated, the system retrieves the poisoned document and uses it to generate misleading, biased, and unfaithful responses to user queries.

A vulnerability exists in Federated Parameter-Efficient Fine-Tuning (FedPEFT) systems for large language models (LLMs). Malicious clients can exploit the PEFT mechanism (e.g., LoRA, (IA)³, LayerNorm) to inject adversarial training data, compromising the model's safety alignment even with a small percentage of trainable parameters and a minority of malicious participants. The attack, termed "PEFT-as-an-Attack" (PaaA), circumvents the LLM's safety guardrails, causing it to generate harmful…