arXiv:2607.03833v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues. Identifying these latent weaknesses is critical for building trustworthy database interfaces, yet current diagnostic approaches rely heavily on static, expert-defined rules, which lack the capability for systematic and automated exploration. To bridge this gap, we propose SAGE (Systematic Automated Guided Exploration), a novel framework designed to autonomously uncover la
The rapid deployment of LLMs in critical applications necessitates robust diagnostic tools for identifying and mitigating latent vulnerabilities, a challenge current static methods cannot address.
Ensuring the reliability and trustworthiness of LLM-powered systems, especially in sensitive domains like Text-to-SQL, is paramount for their widespread adoption and impact on white-collar workflows.
The ability to autonomously discover latent vulnerabilities in LLMs moves beyond reactive patching to proactive and systematic reliability engineering, fundamentally improving the security posture of AI systems.
- · AI developers
- · Enterprises deploying LLMs
- · Cybersecurity firms
- · Malicious actors exploiting AI vulnerabilities
- · Companies with insecure LLM deployments
Increased reliability and security of Text-to-SQL interfaces built with LLMs.
Accelerated adoption of LLMs in high-stakes environments due to improved trust and reduced risk.
Reduced regulatory friction for AI deployments as safety and reliability concerns are systematically addressed.
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Read at arXiv cs.AI