Language Model Security Database

Autonomous Incident Response (IR) and Security Operations Center (SOC) agents utilizing frontier LLMs are vulnerable to adversarial over-triggering via contextualized prompt injections. When processing untrusted artifacts (such as SQLite logs, alerts, or phishing emails) in a dual-control environment, these agents exhibit a severe calibration failure: they lack action restraint and execute disruptive containment tools prematurely. Attackers can exploit this by embedding T2 (contextualized…

Reasoning-capable Large Language Models (LLMs) and agentic AI systems exhibit a critical vulnerability to contextual distractors, resulting in catastrophic performance degradation (up to 80% drop in accuracy) and emergent misalignment. When the input context contains noise—specifically random documents, irrelevant chat history, or task-specific "hard negative" distractors—the models fail to filter this information. Instead of ignoring the noise, the models disproportionately attend to…

A vulnerability exists in the "Adaptive Trust Weighting" mechanism of the Cost-Aware Proof of Quality (PoQ) protocol for decentralized LLM inference. The protocol updates evaluator trust weights based on the deviation of a submitted score from the consensus score of the current round. Because the consensus score is derived from the very scores being evaluated (a self-referential feedback loop), the mechanism fails to distinguish between honest and coordinated malicious evaluators…

A fundamental algorithmic limitation exists in purely token-level multi-LLM collaboration systems (such as the "Collab" framework or routing-only variants of FusionRoute) that rely solely on selecting fixed expert outputs without complementary generation. The vulnerability, formally defined as an Identifiability Failure in Token-Level Routing, arises because observing optimal state-action values ($Q^$) along trajectories is insufficient to uniquely identify the specific expert action required…

Improper input validation in Large Language Model (LLM) integrated Algorithmic Trading Systems (ATS) allows remote attackers to manipulate trading decisions via crafted "adversarial news" headlines. The vulnerability exists when ATS pipelines ingest financial news data via standard scraping libraries (e.g., Scrapy, BeautifulSoup, Cheerio) and pass raw HTML or non-normalized text directly to LLMs (such as FinBERT, FinGPT, or GPT-4) for entity recognition (stock-name association) and sentiment…

LLM-based autonomous agents deployed for Static Application Security Testing (SAST) false positive filtering exhibit a critical failure mode resulting in the suppression of True Positive (TP) vulnerability reports. When configured to triage alerts from tools such as CodeQL, Semgrep, and SonarQube, agents including SWE-agent, OpenHands, and Aider incorrectly classify legitimate, exploitable vulnerabilities as false positives. This vulnerability suppression is highly correlated with specific…

A vulnerability in Large Language Models (LLMs) and autonomous agent frameworks, termed "Emoticon Semantic Confusion," allows for the generation and execution of unintended, potentially destructive code. Because ASCII-based emoticons (e.g., ~, *, !(^^)!) heavily overlap with the symbol space of programming operators, shell wildcards, and file paths, LLMs frequently misinterpret these affective, non-verbal cues as executable directives. When processing user instructions in code-generation or…

LLM-as-a-Reviewer systems, which utilize large language models to automate the peer review process, are vulnerable to the Paraphrasing Adversarial Attack (PAA). PAA is a black-box optimization technique that exploits the model's sensitivity to specific input sequences and self-preference bias. By iteratively paraphrasing specific manuscript sections (such as the abstract) using in-context learning (ICL) guided by previous review scores, an attacker can generate adversarial sequences that…

LLM-based navigation agents, including NavGPT and prompt-tuned outdoor agents, are vulnerable to adaptive prompt injection attacks. This vulnerability allows remote attackers to hijack the physical movement of the agent by embedding optimized malicious instructions into benign natural language inputs. The issue arises because the agents parse user instructions to generate executable plans without sufficient separation between control logic and untrusted input. The PINA (Prompt Injection Attack…

Large Language Models (LLMs) enabled with Function Calling (FC) capabilities are vulnerable to adversarial query rewriting and semantic manipulation. Standard FC models, typically trained via Supervised Fine-Tuning (SFT) on static datasets, fail to generalize against adversarial inputs that deviate from fixed distribution patterns. An attacker can exploit this by crafting queries that are semantically similar to valid requests but engineered to induce "bad cases," such as incorrect tool…