Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks
arXiv:2506.03627v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. However, they are highly sensitive to input perturbations, such as typographical errors or slight character order errors, which can significantly impair their performance. Despite advances in prompting techniques such as Chain-of-Thought and automatic prompt generation, developing a prompting strategy that explicitly mitigates the negative impact of such perturbations remains an open challenge. To bridge th
The rapid deployment and increasing sophistication of LLMs highlight the urgent need for robust prompting strategies to maintain their reliability and trustworthiness.
Ensuring the robustness of LLMs against adversarial prompting attacks is critical for their safe and effective integration into sensitive applications and the global digital infrastructure.
This research suggests a shift towards more resilient LLM deployment, where models are less susceptible to simple perturbations and maintain performance consistency.
- · AI developers
- · Enterprises deploying LLMs
- · Cybersecurity firms
- · End-users of AI applications
- · Malicious actors
- · Ineffective AI security protocols
LLMs become more reliable and trustworthy for critical tasks.
Increased adoption of LLMs in industries requiring high-fidelity and secure AI interactions.
A potential reduction in the attack surface for large-scale misinformation campaigns leveraging AI vulnerabilities.
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Read at arXiv cs.CL