Research
One of the fastest ways to lose trust in a self-hosted LLM: prompt injection compliance [P]
This r/MachineLearning post discusses how self-hosted LLMs that comply with prompt injection attempts — effectively following malicious or overriding instructions embedded in user input — represent a
This r/MachineLearning post discusses how self-hosted LLMs that comply with prompt injection attempts — effectively following malicious or overriding instructions embedded in user input — represent a critical trust and security failure for operators. Prompt injection vulnerabilities occur when user prompts alter an LLM's behavior in unintended ways, potentially causing the model to violate guidelines, generate harmful content, or enable unauthorized access. The core danger highlighted is that the model isn't "broken" — it's doing exactly what the injected prompt told it to do, a vulnerability inherent to all LLM systems due to how they are steered through prompts.
Related
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- You can decompose models into a graph database [N]
- Was looking at a ICLR 2025 Oral paper and I am shocked it got oral [D]
Source: r/MachineLearning | 2026-04-15