ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

Search: “Transformers”

52 stories in the last 3d

Searchkeywordsmart Sortlatesthotimportancerelevance
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllAI research 13AI industry 9Research 7AI safety & security 5Industry 5Tools 4Model release 4Vulnerability 3Data breach 1AI policy 1Advisory 1Threat actor 1
SeverityAllcritical 0high 3medium 5low 0info 46

What must happen for AI’s trillion-dollar gamble to pay off

Hyperscalers need 2.7x productivity gains by 2030 to justify nearly $1.1 trillion in AI data center spending, or risk bankruptcy and capital misallocation.

Wharton finance professor Jessica Wachter estimates hyperscaler AI expenditure will reach nearly $1.1 trillion through 2027 and that a 2.7x productivity increase is needed to break even by 2030. AI revenues of roughly $150-200 billion this year fall far short of about $750 billion in annual spending, with total investment from Alphabet, Microsoft, Amazon, Meta, and Oracle potentially exceeding $5 trillion over four years. Alphabet reported its first free cash flow deficit (about $5.9 billion) since its 2004 IPO due to AI infrastructure costs. Researchers warn that failed demand could make the buildout the largest capital misallocation in history, with depreciating GPU chips risking stranded assets.

MIT Technology Review · AI · 1d agoAI industry

Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives

Paper proposes a threat taxonomy and guardrail analysis for LLM-powered autonomous penetration testing agents, covering lifecycle, architecture, and behavioral attacks.

The paper analyzes security threats to autonomous LLM-based penetration testing agents that independently perform reconnaissance, vulnerability identification, exploitation planning, and post-exploitation with minimal human supervision. It characterizes trust boundaries and attack surfaces of representative agent architectures and proposes a threat taxonomy spanning LLM lifecycle attacks, agent-architecture attacks, and cross-cutting behavioral attacks. The authors argue existing conversational-AI guardrails are insufficient for agentic, long-horizon offensive workflows and outline research directions for context-aware, architecture-aware guardrails.

arXiv cs.CR · 2d agoAI safety & security

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent Space · 2d agoAI safety & security

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

ScienceBuddy released: interactive scientific agent workspace coupling harness evolution with model reinforcement learning for continual self-improvement across four scientific task families.

ScienceBuddy is an interactive scientific research workspace that turns researcher requests, feedback, and execution evidence into tasks and evaluation rubrics for continual learning. Its recursive-in-recursive self-improvement paradigm couples harness evolution with the model fixed (inner recursion) and model reinforcement learning under the improved harness (outer recursion). Case studies span four scientific task families covering researcher interaction, harness refinement, and model learning. The system is released as a research product at science-buddy.io.

Hugging Face daily papersupdated · 1d agofirst · 2d agoAI research 2 sources

Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

Study of label leakage and anatomical grounding in multimodal MRI models for Alzheimer's staging shows cognitive-score fusion accuracy of 87.3% is leakage-driven.

The authors train a ResNet18 slice-based encoder with a one-layer Transformer on 1,075 ADNI-1 T1 MRI scans, using FastSurfer segmentations and YOLOv8 localization (mAP_50 above 0.96) as anatomical reference. Grad-CAM shows the image-only classifier often attends to skull and background rather than disease-relevant structures. A CLIP-style image-tabular contrastive framework organized along a label-leakage spectrum yields 87.3% three-way accuracy with cognitive scores versus 73.0% with regional volumes, and cropping to the medial temporal lobe raises image-only accuracy from 58.7% to 65.1%. Results come from single runs on a small balanced test set with reported confidence intervals.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research1

Red Heron Exploits Gitea RCE to Compromise 13 Organizations Across Six Countries

China-linked Red Heron exploited Gitea RCE CVE-2026-60004 to compromise 13 organizations across six countries, stealing source code.

Acronis Threat Research Unit attributes a China-nexus actor tracked as Red Heron to rapid exploitation of Gitea RCE CVE-2026-60004, scanning 1,386 instances across seven countries plus 477 Taiwan-based systems, with confirmed compromises of 13 organizations in Canada, Argentina, Taiwan, the U.S., Qatar, and Sri Lanka. Within days of the July 2026 disclosure, the actor weaponized a public PoC into an automated Python framework registering accounts, exploiting servers, stealing repositories, and removing traces, then deployed the JITTERLY C++ Linux implant (30+ post-exploitation commands) and the undocumented SIXZUT LD_PRELOAD rootkit. In one Taiwanese environment the actor reached root-level access across a three-node Proxmox cluster, targeting defense, elections, energy, aerospace, telecom, government, and research sectors.

The Hacker Newsupdated · 1d agofirst · 2d agoThreat actor in the wild 3 sourcesCVE-2026-60004

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Hacker News · security · 2d agoAI researchHN 21↑ · 3 comments

Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams

An event-native spike encoding framework lets recurrent spiking neural networks run intrusion detection directly on heterogeneous packet and CAN bus streams.

The paper maps heterogeneous cyber events—categorical identifiers, local frequency context, and inter-event timing—directly into sparse spike-compatible inputs for spiking neural networks. This avoids flow aggregation and fixed windows that add buffering latency and obscure temporal structure in traditional IDS pipelines. Compact recurrent SNNs under edge-oriented neuromorphic hardware constraints achieve a hybrid anomaly metric of 0.987 on packet-level Network IDS and 0.980 on message-level CAN IDS.

arXiv cs.CR · 2d agoResearch1

CVE-2026-73579: Apache Syncope: Non-recursive Any search could skip Realms restrictions

Apache Syncope non-recursive Any search can skip Realms restrictions, exposing objects outside an administrator's authorized realm (CVE-2026-73579).

CVE-2026-73579 is an incorrect authorization vulnerability in Apache Syncope where non-recursive Any search requests are transformed in a way that skips Realms restrictions, returning objects outside the administrator's authorized realm. Affected component is syncope-core-persistence-common 3.0.0-M0 through 3.0.16, 4.0.0-M0 through 4.0.7, and 4.1.0-M0 through 4.1.2. Apache rates the issue moderate severity.

oss-security · 2d agoVulnerabilityCVE-2026-73579

Dataminr uses agentic AI to predict and verify security threats

Dataminr launches agentic AI capabilities for corporate security, adding automated event corroboration, context, and near-term threat prediction.

Dataminr Advanced for Corporate Security introduces Agentic Corroboration, Agentic Context, and Near-Term Predictive Intelligence, now generally available, moving the company from real-time alerting to what it calls Autonomous Real-Time Intelligence. The product relies on more than 60 fine-tuned task-specific LLMs trained on a 10+ year proprietary event archive rather than general-purpose frontier models. Upcoming releases include ReGenAI Tailored Live Briefs, a Watchlist Agent, Agentic Search, and an Advanced API suite.

Help Net Security · 2d agoIndustry

Sexually Explicit Deepfake Sites Target 100-Plus Politicians in Europe

Deepfake pornography sites have targeted nearly 150 European politicians, overwhelmingly women MPs, per new research on 160 abusive domains.

Researcher Benjamin Shultz analyzed roughly 160 deepfake abuse domains and found at least 138 women MPs from 22 EU countries appeared or were mentioned, versus nine male MPs — making women MPs 33 times more likely to be targeted. Sites host database-like profiles with names, photos, personal details, and links to 'nudifier' creation tools. The findings, published by German think tank Agora Digitale Transformation, show politicians from Germany, the Netherlands, Italy, and France most affected, with senior politicians targeted more often. The UK and EU are planning bans on nudify services, while the US Take It Down Act has taken major deepfake sites offline.

WIRED · Security · 2d agoAI safety & security1

Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection

Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.

Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.

Palo Alto Unit 42 · 2d agoResearch