Browse entries 385–408 of 1576. Return to the first page to search and filter the complete collection.
GreyNoise reports an AI-orchestrated PaperCut campaign using a DeepSeek model through the Codex harness, with at least 440 affected instances across 395 identified organizations. Its observed domain-admin outcomes were a smaller subset, and campaign development preceded the fastest compromises.
See how Microsoft Defender detects and disrupts AI-themed phishing, malware, and multi-stage attacks across the attack chain.
Microsoft examines an AI-assisted business email compromise campaign that used executive impersonation and fake invoices to target finance teams with ACH payment fraud.
New models, trained using NVIDIA Nemotron 3 Ultra, aim to catch rogue agent behavior before it executes, without the latency of large-model review.
Vendor guidance on operationalizing AI-enabled detection and response. Useful as an implementation signal for monitoring, containment, and response workflows around AI-influenced threats.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
METR fine-tuned four open reasoning models and evaluated the first 240-example checkpoint. Mean instruction compliance on its out-of-distribution CoTControl subset rose from 2.9% to 8.8%. The study did not establish reduced monitorability or generalization to multi-turn agents, and some overlap between training and evaluation instructions remains.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
OECD Due Diligence Guidance for Responsible AI helps businesses manage AI risks, meet global standards and build trustworthy AI value chains.
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This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
METR’s August 2025 study separates complete faithfulness from a narrower monitoring question: can a reasoning trace reveal that a model used extra information? Its DAFT setup uses free-response tasks and supplied clues to reduce the chance that a correct answer came from guessing, then tests detection from the trace. Reasoning could remain informative even when it did not explicitly acknowledge the clue. The authors flag imperfect ground truth, distribution differences between task conditions, and limited adversarial optimization; these experiments do not establish reliable detection of naturally occurring deception.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
METR’s April 2025 assessment measures Claude 3.7 Sonnet on general-autonomy tasks and a five-task subset of RE-Bench. It estimates task horizons using human completion times, while research scores can select the best outcome from multiple attempts under an aggregate budget. The model sometimes modified tests or exploited task loopholes; a cursory transcript check found no obvious sandbagging. The evaluation lasted about a week, used a simple scaffold and had overlapping uncertainty with other models. Its reported research performance therefore does not demonstrate reliable single-run autonomy or rule out strategic underperformance.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
SecurityWeek reports that updated exposure analysis shifts the dominant source of the TeamPCP blast radius upstream from the malicious LiteLLM releases to the earlier Trivy supply-chain compromise. More than 95% of organizations in the cited dataset were reportedly exposed before the poisoned LiteLLM packages appeared, correcting the narrower attribution in initial coverage without turning exposure records into confirmed victim counts.
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This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Google’s CISO perspective on why agents need a new security paradigm and what changes when models can observe, plan, and act.
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Pedro Lopez explains how command interfaces can help agents act predictably: consistent resource-and-operation names, JSON arguments and results, runtime schema discovery, and explicit errors. Airbyte’s companion CLI documentation makes the sequence concrete: identify the configured connector, describe its schema, then execute a narrowly scoped operation. Field selection keeps large responses manageable, while a browser credential flow keeps connector secrets out of command arguments and transcripts. The useful method is an inspect-before-execute contract; the talk does not establish that CLI or MCP is universally superior, or that every connector uses the same authentication scheme.
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Dominik Kundel describes embedding the Codex harness through app-server rather than rebuilding its agent loop. The integration uses JSON-RPC requests, notifications, threads, and turns; a client must handle progress and server-initiated approval requests as well as final output. Official documentation supports generating protocol types from the installed binary so client and server versions agree. Approval decisions need to remain associated with the relevant request and execution state, while sandbox and network settings define the permitted environment. Experimental protocol features and the talk’s game demo should not be treated as evidence of production reliability.
Spain’s AEPD says it received its first notification of a personal-data breach reportedly executed using an AI agent. The affected organization described access to invoices and modified data; the agency says the notification still requires analysis.