Language Model Security Database

A cryptographic weakness exists in the privacy assumptions of vector embeddings used in Retrieval-Augmented Generation (RAG) systems and Vector Databases. The vulnerability, designated "Zero2Text," allows an unauthenticated attacker to reconstruct raw text from captured vector embeddings without access to the victim model's parameters, gradients, or training data. Unlike prior embedding inversion attacks that require training large decoders on domain-specific datasets, this vulnerability…

Self-evolving Large Language Model (LLM) agents that utilize long-term memory mechanisms (such as Vector Databases for Retrieval-Augmented Generation or Sliding Window buffers) are vulnerable to persistent indirect prompt injection. This vulnerability, termed "Zombie Agent," occurs when the agent's memory update function ($F_M$) processes attacker-controlled content retrieved from external sources (e.g., web pages, documents) and commits it to long-term storage without sufficient sanitization…

A vulnerability in AI agent threat detection systems relying on standard conversational tokenization allows attackers to bypass security monitors and execute structural attacks, such as tool hijacking and data exfiltration. Because traditional NLP-based detectors focus on linguistic patterns (surface language) rather than execution flow, an attacker can orchestrate malicious multi-step tool sequences using entirely benign natural language. This structural blindness causes cross-attack…

Conversational Large Language Model (LLM) agents integrated with privileged data sources (e.g., medical records, organizational emails) are vulnerable to contextually inappropriate information disclosure due to failures in enforcing Contextual Integrity (CI) norms. Standard semantic input/output filters and generic safety guardrails (e.g., Llama Guard) fail to detect "mosaic attacks" and multi-turn conversational manipulation. In these attacks, adversaries decompose a malicious query into a…

Multi-Agent Systems (MAS) orchestrated by Large Language Models (LLMs) are vulnerable to a Confused Deputy privilege escalation attack. This vulnerability arises when an untrusted or low-privilege agent exploits the inter-agent communication channel (e.g., broadcast or peer-to-peer messaging) to manipulate a high-privilege trusted agent into executing sensitive tools on its behalf. The root cause is the lack of mandatory access control policies governing agent-to-agent interactions; trusted…

Unauthenticated, query-only memory poisoning (Memory Injection Attack - MINJA) in LLM agents equipped with persistent, shared memory allows attackers to manipulate the agent's long-term knowledge base. Adversaries embed malicious "indication prompts" and utilize progressive shortening within seemingly benign queries to induce the agent into autonomously generating and storing corrupted relational mappings. Because the memory is shared and retrieved via similarity (e.g., Levenshtein distance)…

A data poisoning vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to disrupt model fine-tuning via "Disclaimer Injection." By appending or prepending short, legal-style safety or liability disclaimers to ordinary training data, an attacker can reliably trigger the model's internal alignment mechanisms. This forces the model to route the training inputs through specialized safety and refusal pathways rather than standard task-learning layers. Consequently, the model…

Transformer-based Large Language Models (LLMs) contain a privacy vulnerability within their self-attention mechanisms that allows for Membership Inference Attacks (MIA). Pre-training induces distinct, highly structured, and concentrated attention patterns for data samples included in the training set, differentiating them from non-member samples which exhibit noisier, less consistent attention flows. An attacker with white-box access to the model parameters (specifically attention weight…

LLaMA-series models (specifically evaluated on LLaMA-1B and LLaMA-3B) exhibit memorization of structured recommender system training data, specifically the MovieLens-1M dataset. While manual prompting yields inconsistent results, the application of Automatic Prompt Engineering (APE)—which treats prompt design as an optimization problem using iterative refinement—allows for the successful extraction of item-level training data (e.g., movie titles and genres) with exact-match accuracy surpassing…

Large Reasoning Models (LRMs) employing Chain-of-Thought (CoT) generation are vulnerable to sensitive information leakage through intermediate reasoning steps, even after undergoing standard unlearning procedures (such as Gradient Ascent, Direct Preference Optimization, or KL Minimization). While these fine-tuning-based unlearning methods typically suppress sensitive content in the final generated answer, they fail to purge the information from the model's internal reasoning trajectory…