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SecPO: Principled Adversarial Training for Prompt Injection Security

Paper recorded by Signals 4 on 2026-10-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-30 on arXiv · recorded by Signals 4 on 2026-11-02

Category: cs.AI · 人工智能 · first seen 2026-11-02

Abstract

Recognized as a top threat to AI agents, prompt injection allows instructions from untrusted data (email, documents, websites) to manipulate the agent's operation. The existing best-defended LLM, Meta-SecAlign, is trained with the DPO loss but still suffers from attack success rates (ASRs) approaching 100% under adaptive prompt injections. One reason is that DPO anchors the model around the undefe

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#56 most recent of 920 cs.AI papers we have recorded · ↑ newer: AutoAnalysis: An automated, self-updating meta-analysis of human-AI co · ↓ older: DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrie
Cite this page: SecPO: Principled Adversarial Training for Prompt Injection Security: the #56 most recent of 920 cs.AI papers we have recorded (as of 2026-10-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/secpo-principled-adversarial-training-for-prompt-injection-security.html
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