Safety in Batches? Understanding and Mitigating Safety Failures in Batch Prompting

• URL: http://arxiv.org/abs/2608.02681v1
• Date: Mon, 03 Aug 2026 01:07:56 GMT
• Title: Safety in Batches? Understanding and Mitigating Safety Failures in Batch Prompting
• Abstract summary: We show that a harmful question reliably refused in isolation can elicit a harmful response when embedded in a batch of benign questions.<n>We identify this as a distinct safety failure mode, not reducible to known vulnerabilities such as in-context learning or long-context effects.<n>We show that batch-aware preference optimization effectively mitigates the vulnerability.
• Score: 31.723003820235448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Batch prompting is a practical inference strategy for large language models, but its safety implications remain underexplored. We show that the success of batch prompting for utility does not extend to safety: a harmful question that is reliably refused in isolation can elicit a harmful response when embedded in a batch of benign questions. We identify this as a distinct safety failure mode, not reducible to known vulnerabilities such as in-context learning or long-context effects, and analyze its causes from two complementary perspectives: alignment signal weakening and refusal signal dilution. Across widely used open-source and frontier commercial models, batch prompting consistently achieves high attack success rates as a simple black-box attack. We further show that batch-aware preference optimization effectively mitigates the vulnerability. These findings highlight a blind spot in current safety alignment and point to batch-aware alignment as a necessary step toward robust deployment.

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