COMMENTARY
With the adoption of artificial intelligence (AI) and machine learning (ML) developing at breakneck speed, security is often a secondary consideration, especially in the context of zero-day vulnerabilities. These vulnerabilities, previously unknown security flaws that are exploited before developers have a chance to fix them, pose significant risks in traditional software environments.
However, if AI/ML technologies are becoming increasingly integrated in business, a new question arises: what does a zero-day vulnerability look like in an AI/ML system, and how does it differ from traditional contexts?
Understanding Zero-Day Vulnerabilities in AI
The concept of an ‘AI zero-day’ is still in its infancy, with the cybersecurity industry lacking consensus on a precise definition. Traditionally, a zero-day vulnerability refers to a flaw that is exploited before it is known to the software maker. In AI, these vulnerabilities often resemble those in standard web applications or APIs, as these are the interfaces through which most AI systems communicate with users and data.
However, AI systems add an extra layer of complexity and potential risk. AI-specific vulnerabilities can potentially include problems such as rapid injection. For example, if an AI system summarizes someone’s email, an attacker could inject a prompt into an email before sending it, causing the AI to return potentially malicious responses. Training data leakage is another example of a unique zero-day threat in AI systems. Using crafted inputs to the model, attackers may be able to extract samples from the training data, which could contain sensitive information or intellectual property. These types of attacks take advantage of the unique nature of AI systems that learn from and respond to user-generated input in a way that traditional software systems do not.
The current state of AI security
AI development often prioritizes speed and innovation over security, leading to an ecosystem where AI applications and their underlying infrastructures are built without robust security from the ground up. This is compounded by the fact that many AI engineers are not security experts. As a result, AI/ML tools often lack the rigorous security measures that are standard in other areas of software development.
From research by the Huntr AI/ML bug bounty communityit is clear that vulnerabilities in AI/ML tools are surprisingly common and may differ from those in more traditional web environments built with current security best practices.
Challenges and recommendations for security teams
As the unique challenges of AI zero-days emerge, the fundamental approach to managing these risks must follow traditional security best practices but be adapted to the AI context. Here are some key recommendations for security teams:
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To adopt MLSecOps: Integrating security practices across the entire ML lifecycle (MLSecOps) can significantly reduce vulnerabilities. This includes practices such as enumerating all machine learning libraries and models in a machine learning BOM (MLBOM), and continuously scanning models and environments for vulnerabilities.
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Conduct proactive security audits: Regular security audits and the use of automated security tools to scan AI tools and infrastructure can help identify and mitigate potential vulnerabilities before they are exploited.
Looking ahead
As AI continues to evolve, the complexity associated with security threats and the ingenuity of attackers will also increase. Security teams must adapt to these changes by incorporating AI-specific considerations into their cybersecurity strategies. The conversation about AI zero-days is just beginning, and the security community must continue to develop and refine best practices in response to these evolving threats.
