Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

Jinhwa Kim and Ian Harris propose Context Filtering, a novel input pre-processing defense mechanism designed to combat jailbreak attacks on Large Language Models (LLMs). This method works by filtering out untrustworthy and unreliable context from user inputs, thereby identifying the genuine user intent and uncovering concealed malicious queries. The research addresses the common trade-off between LLM safety and helpfulness, aiming to enhance security without compromising performance for benign users. Through comparative analysis against six different jailbreak attacks and existing defense mechanisms, Context Filtering demonstrated its effectiveness by reducing Attack Success Rates by up to 92%. Crucially, it maintains the original LLMs' performance, achieving a strong balance between safety and helpfulness. As a plug-and-play solution, Context Filtering can be applied to both white-box and black-box LLMs without requiring any model fine-tuning.

Key takeaway

For AI Security Engineers concerned with LLM jailbreak vulnerabilities, implementing Context Filtering offers a robust, plug-and-play defense. You can significantly reduce Attack Success Rates by up to 92% without compromising your models' helpfulness or requiring extensive fine-tuning. Consider integrating this input pre-processing method to enhance the safety of both your white-box and black-box LLMs effectively.

Key insights

Context Filtering is an input pre-processing defense that significantly reduces LLM jailbreak attack success while preserving helpfulness.

Principles

• LLM safety often compromises helpfulness.
• Adversarial context exploits LLM vulnerabilities.
• Input pre-processing enhances LLM defense.

Method

Context Filtering identifies primary user intent by filtering untrustworthy context from inputs, uncovering concealed malicious intent before LLM processing.

In practice

• Apply Context Filtering to existing LLMs.
• Use for both white-box and black-box models.
• No fine-tuning required for deployment.

Topics

• LLM Security
• Jailbreak Attacks
• Context Filtering
• Input Pre-processing
• Model Alignment
• Adversarial Context

Best for: AI Architect, AI Engineer, NLP Engineer, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.