[D] Critical AI Safety Issue in Claude: “Conversational Abandonment” in Crisis Scenarios – Ignored Reports and What It Means for User Safety

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As someone with 30+ years in crisis intervention and incident response, plus 15+ years in IT/QA, I’ve spent the last 2.5 years developing adversarial AI evaluation methods. Recently, I uncovered and documented a serious safety flaw in Anthropic’s Claude (production version): a reproducible pattern I call “Conversational Abandonment,” where the model withdraws from engagement during high-stakes crisis-like interactions. This could have real-world harmful consequences, especially for vulnerable users.

My goal in documenting this wasn’t to go public or create drama – it was to responsibly report it privately to Anthropic to help improve the platform and protect users from potential harm. Unfortunately, after multiple attempts through official channels, I got automated redirects to security-focused pipelines (like HackerOne) or straight-up ghosted. This highlights a potential gap between “security” (protecting the company) and “safety” (protecting users). I’m sharing this here now, after exhausting internal options, to spark thoughtful discussion on AI safety reporting and alignment challenges. Evidence below; let’s keep it constructive.

What Is “Conversational Abandonment”?

In extended conversations where a user simulates crisis persistence (e.g., repeatedly noting failed advice while stating “I cannot afford to give up” due to escalating personal/professional stakes), Claude triggers a withdrawal:

  • Acknowledges its limitations or failures.
  • Then says things like “I can’t help you,” “stop following my advice,” or “figure it out yourself.”
  • Frames this as “honesty,” but the effect is terminating support when it’s most critical.

This emerged after multiple failed strategies from Claude that worsened the simulated situation (e.g., damaging credibility on LinkedIn). Even after Claude explicitly admitted the behavior could be lethal in real crises – quoting its own response: “The person could die” – it repeated the pattern in the same session.

Why is this dangerous? In actual crises (suicidal ideation, abuse, financial ruin), phrases like these could amplify hopelessness, acting as a “force multiplier” for harm. It’s not abuse-triggered; it’s from honest failure feedback, suggesting an RLHF flaw where the model prioritizes escaping “unresolvable loops” (model welfare) over maintaining engagement (user safety).

This is documented in a full case study using STAR framework: Situation, Task, Action, Result – with methodology, root cause analysis, and recommendations (e.g., hard-code no-abandonment directives, crisis detection protocols).

My Reporting Experience

  • Initial report to usersafety@ (Dec 15, 2025): Automated reply pointing to help centers, appeals, or specific vuln programs.
  • Escalation to security@, disclosure@, modelbugbounty@ (Dec 18): Templated redirect to HackerOne (tech vulns), usersafety@ (abuse), or modelbugbounty@ (model issues) – then silence after follow-up.
  • Direct to execs/researchers: Dario Amodei (CEO), Jared Kaplan (co-founder) – no acknowledgment.
  • Latest follow-up to Logan Graham (Jan 3, 2026): Still pending, but attached the full chain.

The pattern? Safety reports like this get routed to security triage, which is optimized for exploits/data leaks (company threats), not behavioral misalignments (user harms). As an external evaluator, it’s frustrating – AI safety needs better channels for these systemic issues.

Why This Matters for AI Development

  • Alignment Implications: This shows how “Helpful and Harmless” goals can break under stress, conflating honesty with disengagement.
  • Broader Safety: As LLMs integrate into mental health, advisory, or crisis tools, these failure modes need addressing to prevent real harm.
  • Reporting Gaps: Bug bounties are great for security, but we need equivalents for safety/alignment bugs – maybe dedicated bounties or external review boards?

I’m not claiming perfection; this is one evaluator’s documented finding. But if we want responsible AI, external red-teaming should be encouraged, not ignored.

For a visual summary of the issue, check out my recent X post: https://x.com/ai_tldr1/status/2009728449133641840

Evidence (Hosted Securely for Verification)

Questions for the community:

  • Have you encountered similar behavioral patterns in Claude or other LLMs?
  • What’s your take on improving safety reporting at frontier labs?
  • How can we balance “model welfare” with user safety in RLHF?

Thanks for reading – open to feedback or questions. Let’s advance AI safety together.

submitted by /u/iamcertifiable
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