llms ai-safety openai security-vulnerabilities

"Disregard That" Attacks

calpaterson.com

March 25, 2026

10 min read

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53/100

Summary

"Disregard that!" attacks exploit the sharing of context windows in communication, leading to potential security vulnerabilities. These attacks highlight the risks associated with allowing multiple users access to the same AI interaction context.

Key Takeaways

"Disregard that!" attacks exploit vulnerabilities in large language models (LLMs) by manipulating the context window to issue unauthorized commands.
The context window of an LLM includes all input text, such as chat history or coding instructions, which can be compromised if shared with untrustworthy users.
Attempts to secure LLMs with "guardrails" often fail, leading to an ongoing arms race between the model's defenses and potential attackers.
Prompt injection is a common security issue in LLM applications, highlighted by the risks associated with sharing context windows.

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Analysis ready

Community Sentiment

Mixed

Positives

Using an LLM for scheduling tasks like dentist appointments showcases the practical applications of AI in everyday life, enhancing user convenience and efficiency.
Fine-tuning a model for specific tasks can effectively mitigate prompt injection vulnerabilities, demonstrating the importance of tailored training in AI safety.

Concerns

Concerns about prompt injection vulnerabilities highlight significant risks in using general-purpose LLMs, emphasizing the need for improved security measures in AI applications.
The hypothetical approach of separating context windows raises doubts about its feasibility, indicating a gap in understanding LLM architecture and its implications for safety.