Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.
959 research findings · 1077 evaluated models
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Large Language Models (LLMs) are vulnerable to prompt extraction attacks via inversion of their normal outputs. An attacker can train a model to reconstruct the prompt used to generate multiple outputs from an LLM, even without access to internal model parameters (logits) or requiring adversarial queries. This allows extraction of both user and system prompts.
Source: arXiv
A prompt injection vulnerability in OpenAI's custom GPT models allows attackers to extract the system prompt and potentially leak user-uploaded files. Attackers craft malicious prompts that manipulate the LLM into revealing sensitive information, even when defensive prompts are in place. The vulnerability is exacerbated when the model includes a code interpreter.
Source: arXiv
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.