A SHAP-guided heterogeneous ensemble defense framework for financial risk assessment under white-box adversarial attacks

New Archestra's OpenAPPA Saturates Two Major Security Benchmarks with a 0% Attack Success Rate

Archestra released OpenAPPA, an open-source security engine designed to stop data exfiltration caused by prompt injection or model hallucination.

• Archestra launches OpenAPPA, an open-source security engine
• Achieves 0% attack success rate in Bench-Corp and AgentThreatBench benchmarks
• Agentic Permissions Policy Algebra balances security and operational utility

23h ago ·infoq.com

Trump taps Director of National Intelligence Jay Clayton as AI czar: WSJ reports

Director of National Intelligence Jay Clayton will lead the administration's AI policy, The Wall Street Journal reported on Saturday.

23h ago ·cnbc.com

Trump taps Director of National Intelligence Jay Clayton as AI czar: WSJ reports

Director of National Intelligence Jay Clayton will lead the administration's AI policy, The Wall Street Journal reported on Saturday.

23h ago ·cnbc.com

Why 'Local-First' Is the New Stack: How to Build Data-Sovereign AI Apps with Local LLMs, Private Vectors, and Zero-Cloud Dependencies

Originally published on tamiz.pro . For the past decade, the default architecture for software development has been heavily skewed toward the cloud.

• Local-First architecture ensures data stays within user control.
• Advances in local AI technologies enable LLMs to run on consumer hardware.
• Practical implementation uses Python, Ollama, and ChromaDB for zero-cloud AI systems.

23h ago ·dev.to

Sources: AI czar Jay Clayton to chair the WH's AI task force, called "Super Intelligence Force", to pen a report on AI's risks and opportunities within 120 days (Alex Leary/Wall Street Journal)

The White House group will be led by Director of National Intelligence Jay Clayton who will serve as AI czar

23h ago ·wsj.com

'No peanuts' became include_ingredients: ["peanuts"]: a benchmark for tool calls a validator cannot catch

This is a submission for the DEV x Kaggle Benchmarking Challenge . What I Benchmarked An agent I was running called a ranking tool with limit: 8 .

• 30% of responses were syntactically correct but conceptually incorrect
• Models failed to address user needs despite valid schema responses
• Repurposing category showed most frequent misuse of existing parameters

23h ago ·dev.to