Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence

Effective incident response IR is critical for mitigating cyber threats, yet security teams are overwhelmed by alert fatigue, high false-positive rates, and the vast volume of unstructured Cyber Threat Intelligence CTI documents. While CTI holds immense potential for enriching security operations...

Privacy-Preserving LLM Interaction with Socratic Chain-Of-Thought Reasoning and Homomorphically Encrypted Vector Databases

Large language models LLMs are increasingly used as personal agents, accessing sensitive user data such as calendars, emails, and medical records. Users currently face a trade-off: They can send private records, many of which are stored in remote databases, to powerful but untrusted LLM providers...

On Automating Security Policies with Contemporary LLMs

The complexity of modern computing environments and the growing sophistication of cyber threats necessitate a more robust, adaptive, and automated approach to security enforcement. In this paper, we present a framework leveraging large language models LLMs for automating attack mitigation policy...

PT-2024-23319 · Mintplex · Anything-Llm

Name of the Vulnerable Software and Affected Versions: mintplex-labs/anything-llm versions prior to 1.0.0 Description: An improper authorization issue exists in the mintplex-labs/anything-llm application, specifically within the "/api/v/" endpoint and its sub-routes. This flaw allows...