A dark side to LLMs. [Research Saturday]
Sahar Abdelnabi from CISPA Helmholtz Center for Information Security sits down with Dave to discuss their work on "A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models." There is currently a large advance in the capabilities of Large Language Models or LLMs, as well as being integrated into many systems, including integrated development environments (IDEs) and search engines.
The research states, "The functionalities of current LLMs can be modulated via natural language prompts, while their exact internal functionality remains implicit and unassessable." This could lead them to be susceptible to targeted adversarial prompting, as well as making them adaptable to even unseen tasks. Researchers demonstrated these said attacks to see if the LLMs needed new techniques for more defense.
The research can be found here:
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Sahar Abdelnabi from CISPA Helmholtz Center for Information Security sits down with Dave to discuss their work on "A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models." There is currently a large advance in the capabilities of Large Language Models or LLMs, as well as being integrated into many systems, including integrated development environments (IDEs) and search engines.
The research states, "The functionalities of current LLMs can be modulated via natural language prompts, while their exact internal functionality remains implicit and unassessable." This could lead them to be susceptible to targeted adversarial prompting, as well as making them adaptable to even unseen tasks. Researchers demonstrated these said attacks to see if the LLMs needed new techniques for more defense.
The research can be found here:
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