LongPIBench: A Long-Context Benchmark for Prompt Injection
New benchmark reveals that prompt-injection defences tested on short inputs degrade substantially under long-context conditions — a blind spot for enterprises deploying LLMs on large document corpora.
Summary written by editorial AI · Source link below
arXiv:2608.28411v1 Announce Type: new Abstract: Prompt injection attacks pose a serious security risk to large language models in real-world applications. However, existing prompt injection benchmarks primarily focus on short-context inputs, leaving the attacks and defenses in long-context settings largely unexplored. This gap leads to a substantial overestimation of the effectiveness of current defenses. In this paper, we bridge the gap by introducing LongPIBench, a long-context benchmark for
Editorial Analysis
As enterprises feed longer documents into LLM applications, existing prompt-injection defences may silently lose effectiveness, expanding the attack surface for data exfiltration and manipulation.
Test LLM-based applications specifically with long-context prompt-injection scenarios and adjust defence strategies where degradation is observed.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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