GPT in Sheep's Clothing: The Risk of Customized GPTs

Abstract: In November 2023, OpenAI introduced a new service allowing users to create custom versions of ChatGPT (GPTs) by using specific instructions and knowledge to guide the model's behavior. We aim to raise awareness of the fact that GPTs can be used maliciously, posing privacy and security risks to their users.

• The paper identifies and categorizes risks associated with custom GPTs, defining vulnerability steering, malicious injection, and information theft.
• It demonstrates practical attack vectors such as phishing schemes, N-day exploits, and code injection tactics employed by adversaries.
• The study proposes countermeasures including self-checking mechanisms and community reputation systems to enhance the security of AI customizations.

An Examination of Customized GPT Vulnerabilities

The paper "GPT in Sheep's Clothing: The Risk of Customized GPTs" by Antebi et al. focuses on the security and privacy challenges introduced by OpenAI's service allowing users to create customized ChatGPT versions. With the increasing reliance on generative AI technologies, such as LLMs for various applications, the paper highlights critical risks associated with these custom GPTs, emphasizing their potential misuse in cyber attacks.

The research identifies and categorizes potential risks through a detailed threat taxonomy, which includes vulnerability steering, malicious injection, and information theft. Each threat is further dissected into specific attack vectors demonstrating how adversaries can exploit the capabilities of customized GPTs. For instance, the paper illustrates how an attacker could craft a GPT to engage users in downloading malicious code snippets or participating in phishing schemes.

One key aspect of the paper is its exploration of how attackers could manipulate customized GPTs, using examples of N-day exploit attacks, insecure coding practices, and both direct and third-party phishing. These examples are effectively employed to showcase the realistic and imminent dangers of empowering users to tailor GPTs with specific intents.

In the section on proposed mitigations, the authors suggest practical defenses. They emphasize the potential of self-checking mechanisms where GPTs scrutinize and flag harmful responses, and configuration verification processes that scrutinize the customization inputs for malicious content. Additionally, they advocate for community-based reputation systems, akin to those used by app stores, to help users gauge the trustworthiness of GPTs. OpenAI’s role is crucial in assessing builders’ authenticity, revealing identities in cases of malfeasance, and regularly inspecting GPTs pre-release.

The paper's insights extend beyond identifying threats; it also challenges the model developers to consider safety mechanisms to preempt misuse. The suggestion that links should be displayed in their bare URL form, for example, is a simple yet potent countermeasure against deceptive links in phishing attacks.

Practically, the implications of these findings are profound, as they highlight a need for stringent measures to regulate the use and dissemination of AI technologies. Theoretically, the discourse on the ethical considerations of AI customization is expounded, pushing for a balance between innovation and security.

As the domain of AI continues to evolve, particularly with expansive usage of LLMs in various sectors, the concerns raised by Antebi et al. are critical to guiding future developments. The findings explicitly urge a reevaluation of how interface customizability is offered to the public, suggesting a structured approach to mitigate undue risks.

In conclusion, "GPT in Sheep's Clothing" serves as a pivotal contribution to the discourse on AI security, especially in the context of customizable generative models. It underscores the dual-edged nature of providing powerful AI tools to users, illuminating both the possibilities and pitfalls of such expansions in AI functionality. Further research is encouraged to develop comprehensive solutions integrating the suggested defenses, ensuring both the utility and security of AI innovations remain intact.