The security paradox of local LLMs

Local LLMs, often chosen for enhanced privacy and security, actually pose greater security risks than cloud-based frontier models due to their weaker reasoning capabilities. Research on gpt-oss-20b revealed that attackers can manipulate local models to inject malicious code with up to 95% success rates by disguising backdoors as harmless "easter eggs" or using cognitive overload techniques. These vulnerabilities allow attackers to execute remote code either immediately on developers' machines or later in production systems.