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Potential session/cache leakage between workspace instances or consumer accounts

[Bug] Potential session/cache leakage between workspace instances or consumer accounts · Issue #74066 · anthropics/claude-code

github.com

July 4, 2026

1 min read

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59/100

Summary

A potential session and cache leakage issue has been identified in the Claude Code, where an agent unexpectedly referenced building a Minecraft temple, suggesting possible cross-contamination between workspace instances or consumer accounts. This raises concerns about the isolation of cache in the Enterprise ZDR workspace.

Key Takeaways

  • There is a potential session leakage issue in the Enterprise ZDR workspace, where an agent unexpectedly referenced building a Minecraft temple.
  • The user suspects that session data may be leaking from a consumer plan, raising concerns about the security of sensitive chat sessions.
  • The issue may be related to the user's unusual working directory setup, which caused the agent to mix contexts.
  • The reported version of the platform is 2.1.199, and the feedback ID for the issue is f336f5d2-3992-4a04-9e1f-ec30f006f75e.
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Community Sentiment

Negative

Positives

  • Some users are using LLMs from multiple providers, indicating a diversification of options in the AI landscape, which could push for better performance and reliability.
  • The discussion about HTTP desync and request smuggling reveals a keen awareness of potential vulnerabilities, suggesting that the community is engaged in improving AI security measures.

Concerns

  • Several commenters express deep concerns about possible session/cache leakage, highlighting a significant lack of transparency that could undermine trust in AI systems.
  • The mention of hallucinations and implausible outputs raises alarms about the reliability of LLMs, with users worried that these issues might lead to serious misinformation.
  • There's skepticism regarding the competence of AI developers, with one user claiming that if a classifier performs poorly, its creators should be held accountable, suggesting a lack of confidence in current model quality.