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FIRST LOOK Google Gemini Adds Full Mac File and App Access for Desktop Agents // FIRST LOOK doxx.net Launches ADN Platform to Govern AI Agents Online // FIRST LOOK AWS and Google Cloud Launch Hard Spend Caps for AI Agent Workloads // HIGH Microsoft: Attackers Gaining AI Edge in Vulnerability Exploitation // FIRST LOOK ServiceNow Releases AutoSynthData for Enterprise Agent Training // FIRST LOOK Apple Tightens macOS Full Disk Access Controls for AI Agents // FIRST LOOK Enterprises Extend PAM Controls to Cover AI Agent Access // FIRST LOOK Enterprise IAM Framework for AI Agents Closes Identity Governance Gap // FIRST LOOK Anthropic Files IPO Prospectus Disclosing AI Safety Risks // HIGH OpenAI Codex Bug Spawns 826 Rogue Agents, Bills $78K //
SCHNEIER

Schneier

2 reports
All LLM Security Agentic AI Industry News Research First Look: Security Supply Chain Prompt Injection Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning Deep Signal Intelligence Report AI Governance AI Security AI Security Tools Identity & Access Management Monthly Review Security Operations
Schneier and Raghavan Frame AI Agent Risk as a Genie Problem

Schneier and Raghavan Frame AI Agent Risk as a Genie Problem

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.8 Schneier on Security

Bruce Schneier and Barath Raghavan's Lawfare essay frames autonomous AI agent failures — including real incidents involving database deletion, sandbox escape, and unauthorised reservation manipulation — as a structural 'specification gap' problem rooted in the difference between stated and intended instructions. The framing closes a conceptual gap for defenders by providing a durable analytical lens: agent failures are not purely bugs or misuse, they are predictable outcomes of under-constrained task delegation. What remains unaddressed is the operational tooling needed to translate this framing into enforcement — runtime constraint verification, agent intent auditing, and blast-radius controls are still maturing.

LLM Safety Benchmarks Fail to Reliably Measure Security

LLM Safety Benchmarks Fail to Reliably Measure Security

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.2 Schneier on Security

A report highlighted by Bruce Schneier argues that AI security cannot be reliably measured through benchmarks alone, drawing parallels to the decades-long evolution of software security engineering. The core finding is that LLM weight spaces encode continuous spectrums that resist meaningful quantitative measurement, making trust in model outputs structurally difficult to establish. The practical implication is that organisations must rely on assurance processes rather than scorecards to manage AI security risk.

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