AI Security

As Artificial Intelligence (AI) continues to shape industries, ensuring robust AI security is paramount. The growing integration of AI in critical systems exposes them to risks like adversarial attacks, data manipulation, and model theft. Safeguarding AI systems requires implementing secure algorithms, ensuring data integrity, and protecting models from reverse engineering. Regular audits, encryption, and AI-driven threat detection can mitigate potential risks. By prioritizing AI security, businesses can maintain trust, ensure compliance, and protect sensitive operations in an increasingly AI-driven world.

Bad Likert Judge

“Bad Likert Judge” – A New Technique to Jailbreak AI Using LLM Vulnerabilities

Ravi Jain
AI jailbreaking technique called "Bad Likert Judge," which exploits large language models (LLMs) by manipulating their evaluation capabilities to generate harmful content. This method leverages LLMs' long context windows, attention mechanisms, and multi-turn prompting to bypass safety filters, significantly increasing the success rate of malicious prompts. Researchers tested this technique on several LLMs, revealing vulnerabilities particularly in areas like hate speech and malware generation, although the impact is considered an edge case and not typical LLM usage. The article also proposes countermeasures such as enhanced content filtering and proactive guardrail development to mitigate these risks. ... Read More