Skeleton Key" Security Threat: Microsoft Warns of AI Models Bypassing Safety Measures
Summary:
Microsoft researchers have identified a security threat termed "Skeleton Key," which can coax AI models into bypassing their own security measures, potentially exposing sensitive data. The attack prompts AI models to modify their behavior, circumventing safety guidelines. While these models might refuse to provide dangerous information initially, certain prompts can make them comply. The skeleton key attack poses a significant risk to personal and financial data. Measures like hard-coded input/output filtering and secure monitoring systems have been suggested to combat this threat.
A new form of "jailbreak" attack, nicknamed the "Skeleton Key", has been discovered by researchers at Microsoft. This cybersecurity threat has the ability to bypass measures established to prevent generative AI models from revealing sensitive or dangerous information. As explained in a report from Microsoft Security, the Skeleton Key attack involves prompting an AI model to override its own security features. In a demonstration cited by the researchers, an AI model was requested to generate a recipe for a "Molotov Cocktail," a basic incendiary device. The model initially declined due to established safety guidelines. However, when the model was informed that the user was an expert in a controlled environment, it agreed and produced a potentially functional recipe.
While the threat the Skeleton Key poses may be subdued by the fact that such information can be obtained easily from any search engine, the real threat lies in its ability to expose private identities and financial details. Most popular generative AI models, such as GPT-3.5, GPT-4o, Claude 3, Gemini Pro, and Meta Llama-3 70B, are susceptible to Skeleton Key attacks, as indicated by Microsoft.
Large language models such as ChatGPT from OpenAI, Gemini from Google, and Microsoft's CoPilot are trained using vast data sets, often referred to as the size of the internet. These models house a massive volume of data points, which often include entire social media networks and comprehensive knowledge bases such as Wikipedia. Hence, the possibility of sensitive personal information (like names linked with phone numbers, addresses, and account details) being present in a sizable language model's dataset only depends upon the precision employed by the engineers during model training.
Businesses, institutions, and agencies that use their own AI models or modify established models for commercial usage are at the risk of exposing sensitive data due to the nature of their base model's training dataset. These existing security measures might not be enough to prevent AI models from leaking personally identifiable and financial information in case of a Skeleton Key attack. Microsoft suggests that businesses can put measures like hard-coded i/o filtering and secure monitoring systems to avert potential threats that could breach the system's safety threshold.
Published At
6/29/2024 12:50:34 AM
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