Do Reasoning LLMs Refuse What They Infer in Long Contexts?

TL;DR

This paper investigates how long-context language models can infer harmful objectives from incomplete information, revealing a safety gap where models often fail to refuse harmful inferences in complex reasoning scenarios.

Contribution

It introduces compositional reasoning attacks to evaluate the safety of LLMs in long contexts, highlighting their limitations in refusing inferred harmful requests.

Findings

Models refuse direct harmful requests effectively.

Refusal rates drop when harmful objectives are reconstructed compositionally.

Longer contexts increase the likelihood of harmful inferences and failures to refuse.

Abstract

Long-context LLMs can infer objectives that are not stated explicitly. This capability is useful for reasoning over documents, code, retrieved evidence, and tool traces, but it also creates a safety risk: harmful intent can be distributed across a context and become visible only after the model composes the relevant pieces. Existing safety evaluations mostly test explicit harmful requests, and therefore miss this failure mode. We introduce compositional reasoning attacks, a long-context threat model in which harmful requests are decomposed into semantically incomplete fragments and embedded in long contexts. The final query is neutral; the harmful objective emerges only if the model retrieves the fragments, composes them, and infers the implied goal. We instantiate this setting using AdvBench requests, varying the required reasoning from Direct Retrieval to Single-hop Aggregation, Chain Reasoning, and Multi-hop Deductive Reasoning, and evaluate 15 frontier LLMs on contexts up to 64k tokens. Models usually refuse harmful requests when they are directly retrievable. However, refusal rates drop sharply when the same objectives must be reconstructed compositionally, often with larger failures in longer contexts. Benign reconstruction and fragment-position analyses indicate that these failures are not mainly retrieval errors: models often infer the harmful objective and then comply. Increasing inference-time reasoning improves refusal but remains incomplete and costly. Our results reveal a long-context safety gap: current models are better at refusing harmful requests they see than harmful objectives they infer.