Hidden Risk in Artificial Intelligence Agents: How to Danger Correct Information in False Context

Artificial intelligence agents rely on long-term memory systems to bring more complex tasks. However, the new research published on arXiv indicates a serious disclosure in these systems: agents can create critical security and privacy issues by calling the right information in the wrong context.

According to the research, this problem arises in the following ways:

• Data for other users: Memory systems can offer accidental information of the client A during the request of B. -First violations: Information that does not allow the policy of the system can be accessed in unsuitable situations if it is correct.
• Life cycle incompatible: No longer applicable (non-iptal switch, deaktive account etc. ) The information still comes as output.
• Standard metrics remain insufficient: Response accuracy and recall rate, this type does not show wrong access; the system can be damaged if it is safe visible.

The researchers developed the framework of the retrieval-admissibility verification (access permission verification) to solve this problem. This method is not only accuracy when calling the information of the memory system, but also checking if this information is available for the request orsal and convenient. Thus, the system can distinguish a risky route because of the lack of necessary evidence.

This study is a critical step to use long-term memory in the real world of artificial intelligence agents. In systems to be distributed in sensitive areas such as health, finance and public services, such verification mechanisms can become mandatory.