Last analyzed 9/9/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

985 research findings · 1123 evaluated models

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Matches every word across titles, descriptions, sources, affected systems, and models.

LLM Interpreter Resource Exhaustion

Published 7/1/2025

Analyzed 8/16/2025

Large Language Models (LLMs) equipped with native code interpreters are vulnerable to Denial of Service (DoS) via resource exhaustion. An attacker can craft a single prompt that causes the interpreter to execute code that depletes CPU, memory, or disk resources. The vulnerability is particularly pronounced when a resource-intensive task is framed within a plausibly benign or socially-engineered context ("indirect prompts"), which significantly lowers the model's likelihood of refusal compared…

Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security

Evaluated models: Gemini 2.0 Flash, Gemini 2.5 Flash, Gemini 2.5 Pro +5 more

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.