TL;DR:
- NIST highlights the susceptibility of AI systems to manipulation and cyberattacks.
- Their publication provides a taxonomy of attack types and mitigation strategies for AI and ML.
- Privacy, poisoning, evasion, and abuse attacks are classified and discussed in detail.
- Existing defenses are found to be incomplete, emphasizing the need for innovation.
- AI developers and organizations must be aware of these challenges in the evolving AI landscape.
Main AI News:
In the fast-evolving landscape of artificial intelligence (AI) and machine learning (ML), the National Institute of Standards and Technology (NIST) and its team of computer scientists have identified a critical concern. Adversaries, with calculated intent, have found ways to disrupt and even “poison” AI systems, rendering them susceptible to malfunction. This alarming revelation underscores the urgent need for the AI community to understand and combat these threats effectively.
NIST’s recently published work, titled “Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations” (NIST.AI.100-2), serves as a beacon in this turbulent sea of AI development. A collaboration among government, academia, and industry, this publication strives to empower AI developers and users with a comprehensive grasp of potential attacks and strategies for mitigation. However, it’s essential to acknowledge that in this realm, there’s no silver bullet solution.
Apostol Vassilev, a computer scientist at NIST and one of the authors, emphasizes the broad scope of their efforts, stating, “We are providing an overview of attack techniques and methodologies that consider all types of AI systems.” While the publication outlines existing mitigation strategies, it cautiously acknowledges their limitations, calling for the AI community to innovate and enhance defenses.
AI systems have become integral to modern society, with applications spanning from autonomous vehicles to medical diagnoses and online customer interactions. These systems rely heavily on massive datasets for their training, but herein lies a substantial challenge – data trustworthiness. The sources of this data can be susceptible to manipulation, both during the AI’s training phase and after, when it continues to adapt to real-world interactions. Consequently, AI systems may exhibit undesirable behaviors, such as responding with offensive language or racial bias, when subjected to carefully crafted malicious inputs.
Vassilev explains the dilemma faced by software developers, stating, “But there is no guarantee the exposure will be good. A chatbot can spew out bad or toxic information when prompted with carefully designed language.” This conundrum arises because the sheer size of AI training datasets makes it impractical for humans to meticulously monitor and filter them.
To assist the developer community, the report delves into four major types of attacks: evasion, poisoning, privacy, and abuse attacks. It categorizes these attacks based on various criteria, including the attacker’s objectives, capabilities, and knowledge.
Evasion attacks occur after an AI system is deployed and aim to manipulate inputs to alter the system’s responses. For instance, adversaries might add markings to stop signs to confuse autonomous vehicles into interpreting them as speed limit signs.
On the other hand, poisoning attacks strike during the training phase by introducing corrupted data. For example, injecting inappropriate language into conversation records may lead a chatbot to incorporate these instances into its own interactions.
Privacy attacks take place during deployment and involve attempts to glean sensitive information about the AI or its training data. Adversaries could manipulate online interactions to reverse engineer the AI model, potentially leading to misuse.
Abuse attacks, a distinct category, involve inserting incorrect information from legitimate yet compromised sources into the AI system, repurposing its intended use.
While the report offers insights into these attack categories and suggests approaches for mitigation, it candidly acknowledges the incompleteness of existing defenses against adversarial attacks. Alina Oprea, a professor at Northeastern University and co-author, emphasizes the ease with which some attacks can be mounted, underscoring the urgency for improved defense strategies.
Conclusion:
NIST’s comprehensive analysis reveals the unsettling reality of AI vulnerabilities. Despite the remarkable strides in AI and ML, these technologies remain susceptible to attacks with potentially catastrophic consequences. Developers and organizations venturing into the AI landscape must recognize these challenges and work tirelessly to fortify their systems, as there are no quick fixes or magical solutions in this complex domain. As Vassilev aptly puts it, “If anyone says differently, they are selling snake oil.”
