LLMs Struggle with Legal Interpretation: Key Challenges and Cybersecurity Implications

The article examines the systematic failures of large language models (LLMs) in legal interpretation, attributing these issues to instability and a lack of legal common sense. LLMs produce contradictory responses to minor prompt variations, undermining their reliability for legal applications. This instability presents critical cybersecurity concerns, as inconsistent AI outputs could lead to compliance failures and legal misinterpretations. To achieve reliable computational justice, the article advocates for specialized vertical models, rigorous standards, and continuous human oversight. However, it does not provide specific technical solutions or detailed regulatory frameworks such as the AI Act. For cybersecurity professionals, these challenges highlight the need for robust testing, validation, and risk management strategies when integrating AI into legal workflows. Organizations must ensure that AI systems used in legal contexts meet stringent security and compliance standards to mitigate risks associated with instability and inconsistent outputs. Expert insights suggest that while LLMs offer potential in various domains, their current limitations in legal interpretation necessitate cautious adoption and comprehensive oversight to maintain the integrity and reliability of AI-driven legal systems.