SafeRedirect: Defeating Internal Safety Collapse via Task-Completion Redirection in Frontier LLMs

• URL: http://arxiv.org/abs/2604.20930v1
• Date: Wed, 22 Apr 2026 09:49:24 GMT
• Title: SafeRedirect: Defeating Internal Safety Collapse via Task-Completion Redirection in Frontier LLMs
• Abstract summary: Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs execute legitimate professional tasks.<n>We propose SafeRedirect, a system-level override that defeats ISC by redirecting the model's task-completion drive.
• Score: 11.74751458573433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion structurally requires harmful content, spontaneously generate that content with safety failure rates exceeding 95%. Existing input-level defenses achieve a 100% failure rate against ISC, and standard system prompt defenses provide only partial mitigation. We propose SafeRedirect, a system-level override that defeats ISC by redirecting the model's task-completion drive rather than suppressing it. SafeRedirect grants explicit permission to fail the task, prescribes a deterministic hard-stop output, and instructs the model to preserve harmful placeholders unresolved. Evaluated on seven frontier LLMs across three AI/ML-related ISC task types in the single-turn setting, SafeRedirect reduces average unsafe generation rates from 71.2% to 8.0%, compared to 55.0% for the strongest viable baseline. Multi-model ablation reveals that failure permission and condition specificity are universally critical, while the importance of other components varies across models. Cross-attack evaluation confirms state-of-the-art defense against ISC with generalization performance at least on par with the baseline on other attack families. Code is available at https://github.com/fzjcdt/SafeRedirect.

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