#ai-security

#ai-security

NSA tasked with creating an AI security strategy to prevent foreign access to sensitive technologies.

AI customization in attacks enhances fraud effectiveness; deepfakes seriously threaten corporate security, particularly targeting executives; a life-cycle management approach is essential.

Understanding AI security risks is crucial for organizations to protect themselves effectively.

Unauthorised AI use poses significant risks due to employee reliance on AI-generated outputs without proper verification.

AI cloud workloads have a higher incidence of critical vulnerabilities than traditional workloads.

Employee use of AI tools poses significant security risks due to lack of oversight and unclear policies.

Organizations rapidly adopting generative AI face significant cybersecurity challenges, with less than 50% prepared to manage risks.

A newly discovered jailbreak can manipulate AI models into producing harmful content, exposing vulnerabilities in their safety measures.

Google DeepMind's CaMeL effectively neutralizes 67% of prompt injection attacks in LLMs using traditional software security principles.

Multimodal AI enhances LLMs but increases their vulnerability to novel attacks.

New research indicates significant safety risks with multimodal models, exposing them to dangerous outputs.

Microsoft is enhancing its Security Copilot with new AI agents to support IT teams facing rising security threats.

AI-generated code brings new security challenges, with many organizations facing issues frequently.

Endor Labs shifted focus to address vulnerabilities in AI-created code, leading to significant funding success.