Microsoft Azure’s Russinovich sheds light on key generative AI threats
by nlqip
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The Microsoft Azure CTO revealed that just by changing 1% of the data set — for example, using a backdoor — an attacker could cause a model to misclassify items or produce malware. Some of these data poisoning efforts are easily demonstrated, such as the effect of adding just a small amount of digital noise to a picture by appending data at the end of a JPEG file, which can cause models to misclassify images. He showed one example of a photograph of a panda that, when enough digital noise was added to the file, was classified as a monkey.
Not all backdoors are evil, Russinovich took pains to mention. They could be used to fingerprint a model which can be examined by software to ensure its authenticity and integrity. This could be oddball questions that are added to the code and unlikely to be asked by real users.
Probably the most infamous generative AI attacks are concerned with prompt injection techniques. These are “really insidious because someone can influence just more than the current dialog with a single user,” he said.
Russinovich demonstrated how this works, with a piece of hidden text that was injected into a dialog that could result in leaking private data, and what he calls a “cross prompt injection attack,” harking back to the processes used in creating web cross site scripting exploits. This means users, sessions, and content all need to be isolated from one another.
The top of the threat stack, according to Microsoft
The top of the threat stack and various user-related threats, according to Russinovich, includes disclosing sensitive data, using jailbreaking techniques to take control over AI models, and have third-party apps and model plug-ins forced into leaking data or getting around restrictions on offensive or inappropriate content.
One of these attacks he wrote about last month, calling it Crescendo. This attack can bypass various content safety filters and essentially turn the model on itself to generate malicious content through a series of carefully crafted prompts. He showed how ChatGPT could be used to divulge the ingredients of a Molotov Cocktail, even though its first response was to deny this information.
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The Microsoft Azure CTO revealed that just by changing 1% of the data set — for example, using a backdoor — an attacker could cause a model to misclassify items or produce malware. Some of these data poisoning efforts are easily demonstrated, such as the effect of adding just a small amount of digital noise…
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