Artificial intelligence (AI) promises to transform major sectors like healthcare, transportation, finance, and government over the coming years. But the advanced machine learning (ML) models powering this AI revolution also introduce new vectors of attack for malicious actors. As adoption accelerates, so too do emerging cybersecurity risks.
That troubling dynamic motivates a comprehensive new report on AI security published by the U.S. National Institute of Standards and Technology (NIST). The report maps out a detailed taxonomy of current adversarial threats to AI systems across different modalities such as computer vision, natural language processing, speech recognition, and tabular data analytics. It also summarizes known techniques to mitigate these multifaceted threats.
The core conclusion is striking: virtually every component of the machine learning pipeline is vulnerable. These components include:
- Training data collection and preprocessing
- Model development environments
- Training procedures
- Model testing harnesses
- Live deployment architectures
- Monitoring and observability tools
Threats manifest across this entire stack. For instance, attackers can poison anomalously labeled training data to skew learned correlations. Small disruptions to input data can also cause confident misclassifications post-deployment.
Prominent attack categories highlighted in the report include:
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Data Poisoning – Manipulate training data to degrade test set performance. Poisoning is highly dependent on attacker knowledge and capability. But it can enable subsequent system manipulation.
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Evasion – Carefully craft adversarial examples to cause predictive AI systems to make mistakes during inference by exploiting model vulnerabilities. These inference-time attacks only require interface access.
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Privacy – Reveal sensitive attributes about training data via model outputs or overlay user data onto public datasets to determine what personal information is unintentionally memorized. Membership inference attacks determine if a specific data point was used in model development.
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Abuse – Trick generative AI models like large language models (LLMs) into producing toxic, biased, or misleading content by exploiting weaknesses in prompt programming and output filtering.
[RELATED: Safeguarding Ethical Development in ChatGPT and Other LLMs]
The report stresses that defenses like multi-modal training, data profiling, supply chain authentication, and behavior monitoring do help, but tradeoffs abound. And fundamentally, the very statistical nature of machine learning prevents full security guarantees. There are also economic disincentives for providers to comprehensively address threats that don't impact their bottom line.
While ML developers rapidly innovate protections, attack methods advance in parallel as more bad actors enter the space. Tensions between transparency, accuracy, robustness, and fairness won't be resolved overnight, either. Continuous risk assessment and governance throughout the AI system lifecycle remains essential.
By establishing a shared language and threat taxonomy, NIST aims to accelerate progress towards secure and trustworthy AI resistant to adversarial exploits. But constant vigilance from stakeholders across public, private, and civil spheres is indispensable to fulfill AI's immense promise while keeping risks in check. This effort only grows more critical as AI capabilities achieve new heights.
Read the full report from NIST here.
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