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Lawmakers call for investigation into impact of CISA staffing cuts

Democratic lawmakers asked the GAO to investigate how CISA's roughly one-third workforce reduction affects its mission and critical-infrastructure protection.

Democratic lawmakers led by House Homeland Security ranking member Bennie Thompson asked the Government Accountability Office to examine how recent staffing cuts at CISA affect its ability to protect critical infrastructure and respond to cyber and physical threats. Nearly 1,000 CISA employees have been fired or quit since the Trump administration began, and acting director Nick Andersen plans to hire 300. The letter also flags the FY2027 budget proposal to eliminate nearly 900 additional positions and cut more than $700 million from the agency. CISA has had no confirmed director since Jen Easterly departed, and GAO confirmed it received the request.

The Record · 26d agoPolicy & legal

Lawmakers seek watchdog review of federal hacking of Americans

Sen. Wyden and Rep. Casar asked the GAO to review the federal government's use of spyware and hacking tools against Americans.

Sen. Ron Wyden and Rep. Greg Casar sent a letter to the Government Accountability Office requesting a review of federal law enforcement hacking operations, including spyware use, Rule 41 hacking powers, and acquisition of hacking tools. The letter cites ICE's confirmed work with spyware vendor Paragon and concerns about abuse of invasive surveillance capabilities. The lawmakers note the government publishes no annual reports on hacking operations unlike wiretaps.

CyberScoop · 26d agoPolicy & legal

Mecka AI nears $500M valuation in Sequoia-led deal amid rush for robot training data

Mecka AI, which collects human motion data for robot training, is nearing a Sequoia-led round at about a $500 million valuation.

Mecka AI is nearing a new funding round led by Sequoia Capital at a valuation of roughly $500 million, three months after raising $60 million led by Framework Ventures with participation from Menlo Ventures, SV Angel, and Kindred Ventures. Founded in 2024 by Josh Gao, Mogen Cheng, Jason Chong, and Duy Nguyen, the startup pays people to record everyday tasks using body sensors and smartphones to produce egocentric training data for humanoid robots. The company projected a $100 million annual run rate by the end of 2026 and competes with firms like Scale AI, Mercor, Surge, Micro1, and XDOF in the physical-world data market.

TechCrunch · AI · 5d agoAI industry1

Treasury’s Scott Bessent says no liability exemptions for AI labs

Treasury Secretary Scott Bessent urged Congress to reject AI labs' requested liability exemptions, arguing creator liability is the best safety guarantee.

Testifying before the House Financial Services Committee, Treasury Secretary Scott Bessent said the government should not grant frontier labs liability waivers, responding to Anthropic CEO Dario Amodei's slowdown essay. He cited Treasury's AI safety work since the release of Anthropic's Mythos model, whose cybersecurity risks prompted an April meeting, and coordination with banks and labs after the July Hugging Face cyberattack. Bessent also highlighted the Gold Eagle clearinghouse run with CISA and called for more US-built open-source models to counter China.

CyberScoop · 18h agoAI policy

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

PlannerForge unifies scenario-based testing of autonomous driving motion planners in one LLM-agent framework, outperforming prior baselines.

PlannerForge is an LLM-agent framework that covers the full scenario-based testing pipeline for autonomous driving systems, spanning scenario generation, selection, modification, routing, planner testing, plus new enhancement and benchmarking stages. In evaluations with 10 off-the-shelf LLMs, best-per-task scores range from 0.88 to 1.00, and open-source 20-35B backends such as Qwen3.6:35B match commercial APIs on most tasks. End-to-end chaining retains 83% (commercial) and 78% (open) of seed queries, beats Scenario Factory 2.0 on executable generation, and cost-tuning lifts planner success from 50.4% to 70.2% while cutting collisions from 19.0% to 8.4%.

Hugging Face daily papers · 9d agoAI research