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.

AI security threats

How Cybercriminals Are Weaponizing Misconfigured AI Systems

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"Securing AI: A Guide to Protecting Artificial Intelligence Systems," explores the escalating threats posed by cybercriminals targeting misconfigured AI systems. It details how attackers exploit vulnerabilities in AI infrastructure, such as exposed Jupyter notebooks and weak authentication, to launch sophisticated, AI-powered attacks like prompt injection and model poisoning. The guide outlines various attack vectors across Linux and Windows environments and emphasizes the long-term impact of compromised AI models. Finally, it presents comprehensive detection and prevention strategies, including infrastructure hardening, AI-specific security measures, and enterprise security frameworks, along with services offered by Technijian to address these critical security challenges. ... Read More
Bad Likert Judge

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

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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