arXiv:2510.02480v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can be influenced by harmful or irrelevant context, which can significantly harm model performance on downstream tasks. This motivates principled designs in which LLM systems include built-in mechanisms to guard against such "garbage in, garbage out" scenarios. We propose a novel approach to limit the degree to which harmful context can degrade model performance. First, we define a baseline "safe" behavior for the model -- the model's performance given no context at all (zero-shot). Next, we apply distributi
The proliferation of Large Language Models (LLMs) and their deployment in critical applications necessitates robust mechanisms to ensure reliability and prevent adverse outcomes from corrupted inputs.
This development addresses a fundamental vulnerability of LLMs, enhancing their trustworthiness and enabling broader, safer integration into sensitive environments and autonomous systems.
The ability to mitigate the impact of harmful context will lead to more resilient and predictable LLM performance, reducing the risk of 'garbage in, garbage out' scenarios in real-world applications.
- · AI developers and researchers
- · Enterprises deploying LLMs for critical tasks
- · Users of AI-powered applications
- · Cybersecurity sector
- · Actors relying on context poisoning to compromise AI
- · Low-quality data providers
Increased reliability and adoption of LLMs in diverse and sensitive domains.
New standards and best practices for context handling and robustness in AI systems will emerge.
Enhanced trust in AI could accelerate the development and deployment of fully autonomous AI agents, as one aspect of their reliability is improved.
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