arXiv:2606.26036v1 Announce Type: new Abstract: Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization, where small task-specific datasets exert disproportionate influence on model behavior. In this setting, adversaries manipulate fine-tuning data to induce persistent summarization failures, such as biased or harmful summaries, while preserving standard evaluation metrics. We present a unified post-hoc defense framework for detecting and remediating fine-tuning-stage poisoning in summarization model
The proliferation of LLMs and their fine-tuning on diverse datasets makes them vulnerable to adversarial attacks, necessitating immediate defense mechanisms.
Data poisoning can compromise the reliability and trustworthiness of AI systems, particularly in critical applications like summarization, leading to biased or harmful outputs.
The development of robust defense frameworks will enhance the security and integrity of LLMs, enabling safer and more trustworthy AI deployments.
- · AI security researchers
- · Organizations deploying LLMs
- · Users of summarization models
- · Malicious actors
- · Vulnerable LLM deployments
Increased focus on adversarial robustness in AI research and development.
New regulatory frameworks and best practices emerge for securing AI fine-tuning processes.
The development of 'AI immune systems' that automatically detect and neutralize threats in deployed models.
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Read at arXiv cs.CL