Rapid development of artificial intelligence is subject to a number of threats including misdirection, data poisoning and privacy attacks according to the National Institute of Standards and Technology (NIST).
A report from NIST states that hostile actors can attack and confuse AI systems and there is no way to fully protect against it.
Evade, poison, abuse
Due to the massive data sets used to train large language models (LLM), it is not possible to fully audit all of the data being fed to an AI to train it, leaving vulnerabilities in the accuracy of the data, its content, or how it will respond to certain queries.
AI can be targeted during its training in an attack known as poisoning, which involves the AI recognizing obscene language as a common part of communication by throwing in swear words and toxic language into training material. In the past, AI trained on poisoned data have quickly become racist and derogatory in their responses to certain questions
There are also concerns that evasion attacks could target AI post-deployment by changing its recognition of inputs, or how an AI responds to an input. One example given in the publication is adding additional markings to a stop sign at an intersection, causing a self-driving car to not recognize the sign, potentially causing an accident.
The publication also highlights that the sources used to train AI can be identified by reverse engineering its responses to queries, and then adding malicious examples or information to these sources prompting inappropriate responses from the AI.
Finally, it is possible for malicious actors to compromise a legitimate source of information used by the AI and edit its contents to change the AI’s behavior so that it no longer works within the context of its intended use.
The most worrying part of these attacks, the publication notes, is that these attacks can be done with “black-box” knowledge. Black-box implies that the attackers require very little knowledge of AI systems in order to carry out a successful attack. White-box would imply full knowledge of a system, and a partial knowledge is known as gray-box.
One of the authors of the publication, NIST computer scientist Apostol Vassilev, said, “We are providing an overview of attack techniques and methodologies that consider all types of AI systems.
“We also describe current mitigation strategies reported in the literature, but these available defenses currently lack robust assurances that they fully mitigate the risks. We are encouraging the community to come up with better defenses.”
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