A review of a Google paper outlining their framework for secure AI agents, focusing on risks like rogue actions and sensitive data disclosure, and their three core principles: well-defined human controllers, limited agent powers, and observable actions/planning.
This article discusses a new paper outlining design patterns for mitigating prompt injection attacks in LLM agents. It details six patterns – Action-Selector, Plan-Then-Execute, LLM Map-Reduce, Dual LLM, Code-Then-Execute, and Context-Minimization – and emphasizes the need for trade-offs between agent utility and security by limiting the ability of agents to perform arbitrary tasks.
The article details five security vulnerabilities in the Model Context Protocol (MCP): Tool Poisoning, Rug-Pull Updates, Retrieval-Agent Deception (RADE), Server Spoofing, and Cross-Server Shadowing. It explains how these vulnerabilities could compromise user safety and data integrity in AI agent systems.
Cisco and Meta are championing open-source large language models (LLMs) for enterprise threat defense, announcing new models and initiatives at RSAC 2025. Cisco's Foundation-sec-8B LLM and Meta's AI Defenders Suite aim to provide scalable, secure, and cost-effective cybersecurity solutions through collaboration and open innovation.
This article discusses the importance of integrating responsible AI practices with security measures, particularly within organizations like Grammarly. It emphasizes treating responsible AI as a product principle, securing the AI supply chain, and the interconnectedness of responsible AI and security. It also touches on the future of AI customization and control.
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The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
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The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
A court in India has ordered the blocking of encrypted email provider Proton Mail across the country following a complaint about obscene content. This is the latest in a series of legal challenges Proton Mail has faced in India.
GoSearch is an efficient and reliable OSINT tool designed for uncovering digital footprints associated with a given username. It's fast, straightforward, and dependable, enabling users to track an individual's online presence across multiple platforms. It integrates data from HudsonRock's Cybercrime Intelligence Database, BreachDirectory.org, and ProxyNova's database.
This article details setting up intrusion detection and prevention systems (IDS/IPS) on an OPNsense router, covering options like CrowdSec, Suricata, and ZenArmor. It explains the differences between IDS and IPS, provides setup instructions for ZenArmor and CrowdSec, and recommends using these tools for enhanced network security.
DeepMind is prioritizing readiness, proactive risk assessment, and collaboration with the wider AI community as they explore the frontiers of AGI, focusing on mitigating risks like misuse and misalignment.
Researchers at HiddenLayer have developed a novel prompt injection technique that bypasses instruction hierarchy and safety guardrails across all major AI models, posing significant risks to AI safety and requiring additional security measures.
