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When Agents See Differently: Exposing UI Desynchronization Threats in Mobile Agents

Researchers expose 'human-agent UI desynchronization' attacks where repackaged APKs invisibly mislead mobile AI agents into attacker-chosen actions.

The paper introduces human-agent UI desynchronization: agents ingest digital screenshots and accessibility metadata that reveal content human users cannot perceive due to occlusion and luminance-contrast limits. An automated framework embeds perturbations into repackaged APK clones that steer mobile agents toward attacker-designated actions without access to runtime user instructions or online adaptation. Evaluations across five mobile-agent frameworks and three backbone models on 546 tasks achieved average misleading rates of 77.9% and 66.9%. A questionnaire study with 186 participants found the visual perturbations difficult for humans to notice.

arXiv cs.CR · 1d agoAI safety & security

The democratization of cyber warfare — and what it means for CISOs

CSO Online argues AI is democratizing cyber warfare, citing Taiwan's first largely autonomous AI-driven attack that hit 85 government accounts.

CSO Online argues AI is accelerating the democratization of cyber warfare by collapsing cost and skill barriers, with effects already reaching the private sector. It cites the August disclosure of the first largely autonomous AI-enabled attack on Taiwan government infrastructure, where up to eight agents operating simultaneously compromised at least 85 government accounts and exfiltrated more than 2,500 personnel records before expanding to the nuclear safety agency and energy companies. The piece places this in a historical arc from crossbows to cheap drones and cites Kane Gamble's 2015 solo social-engineering compromise of senior US intelligence officials as evidence that low-resource attackers were already viable.

CSO Online · 12d agoIndustry

deepseek-ai/DeepSeek-V4-Flash-Vision-Exp — new model trending #10 on Hugging Face

DeepSeek released DeepSeek-V4-Flash-Vision-Exp, its first experimental multimodal model, with large multimodal agent benchmark gains over V4-Flash-0731.

DeepSeek AI published DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal model built on the DeepSeek-V4-Flash architecture with added visual modules and continued training. It scores 83.9 on Terminal Bench 2.1 and 36.5 on ApexBench Pass@1 versus 26.2 for DeepSeek-V4-Flash-0731, while remaining comparable to Opus-4.8 on several benchmarks. The MIT-licensed repository ships a tokenizer, OpenAI-style and TXT prompt encoding, and a minimal PyTorch inference implementation, with vLLM and SGLang deployment recipes.

Hugging Face trending models · 16d agoModel release1

PRs NOT Welcome: How Top AI Open Source Projects Are Managing Thousands of Contributors

Top AI open source projects like Vercel, Astro, Flue, and tldraw are restricting external PRs and using agent-based software factories to triage, fix, and review contributions.

Several prominent AI-native open source projects are closing or limiting external pull requests, largely because submissions are often AI-generated. Vercel built a multi-agent software factory for its AI SDK (over 20 million weekly npm downloads) that now authors 25-35% of merged PRs and closes 70-80% of issues. Astro adopted similar auto-triage automation, Fred Schott created the Flue framework with automatic PR-to-issue conversion, and tldraw automatically closes external PRs.

Latent Space · 14d agoAI industry

Infostealers Target Claude, Cursor, Codex and Other AI Agents to Steal Credentials and Sensitive Data

Gen Digital researchers report infostealer families Amatera, Remus and CallbackBeaver now harvest Claude, Cursor and Codex agent data, including tokens and MCP configs.

Gen Digital researchers found commodity infostealers extending their collection rules to local AI coding agent data from Claude, Cursor, Codex, Cline, Continue and OpenCode on Windows and macOS. Amatera targets Cline and Continue, Remus targets Claude, Cursor and OpenCode, and CallbackBeaver added Claude and Cursor with more than 5,000 samples observed in 30 days; Djinn Stealer hits Claude, Codex, Gemini, Cline, OpenCode and Kilo on macOS. Stolen data includes access and refresh tokens, prompt histories, conversation databases and MCP configurations holding API keys, potentially exposing connected source-control, cloud and ticketing systems. Remus is assessed as a Lumma Stealer variant using EtherHiding C2 resolution via Ethereum smart contracts.

GBHackers · 7d agoMalware in the wild1

Akeyless adds real-time enforcement for AI agents in production

Akeyless launches Agentic Runtime Authority, enforcing real-time intent-based access control on AI agent actions, with integrations for Claude Enterprise, OpenAI Codex and Amazon Bedrock AgentCore

Akeyless announced general availability of Agentic Runtime Authority, a real-time identity control layer that evaluates AI agent actions and blocks policy-violating actions before execution, layered on top of its SecretlessAI credential brokering. New integrations cover Claude Enterprise, OpenAI Codex and Amazon Bedrock AgentCore, alongside an Agentic Access Dashboard with session kill switches and event forwarding to Splunk, Datadog and Microsoft Sentinel. The vendor's platform already secures over 220 billion machine identity interactions for Fortune 500 organizations.

Help Net Securityupdated · 5d agofirst · 6d agoTools 2 sources1

AgenticGen: Reward-Guided Agentic Video Generation for Advertising

AgenticGen applies DPO and GRPO reward-guided reasoning to ad video generation, improving TikTok CTR 2.72%, CVR 2.63%, and Advv 9.61%.

AgenticGen decomposes advertising video generation into two trainable reasoning stages, strategy selection and draft generation, supervised by online business feedback. It learns a performance-based reward from accumulated online feedback plus a rubric-based reward aligned with human quality standards, then optimizes policies with DPO followed by GRPO using process and outcome rewards. Online A/B experiments in the TikTok advertising system show CTR up 2.72%, CVR up 2.63%, and Advv up 9.61% over an SFT baseline.

Hugging Face daily papers · 16d agoAI research

Unit 42 warns AI has shifted balance of power from defenders to attackers

Unit 42 says agentic AI has shifted attacker advantage, investigating an incident where one attacker exploited 50 enterprise applications in under 10 hours.

Palo Alto Networks Unit 42 leaders said early waves of agentic AI-enabled attacks are breaking in the wild and that frontier model capabilities have shifted the balance of power from defenders to attackers. The team is actively investigating an attack on a customer where an attacker used an agentic framework to exploit 50 applications and other weaknesses across the enterprise in less than 10 hours, work they estimate would have taken at least 10 days pre-AI. Unit 42 says AI already touches the entire attack chain, including malware development, social engineering, and ransomware negotiations. The warning follows April's Project Glasswing initiative formed with Anthropic around its Mythos model.

CyberScoop · 19d agoAI safety & security in the wild

Embodied-BenchForge: A Closed-Loop Agentic Workflow for Embodied Benchmark Construction

Embodied-BenchForge automates embodied benchmark construction via closed-loop synthesis with verification and repair, yielding seven benchmarks for MLLM evaluation.

Embodied-BenchForge is an agentic framework that transforms user-specified evaluation intents into complete embodied benchmark artifacts via Closed-Loop Benchmark Synthesis. Skill-Orchestrated Artifact Synthesis composes typed reusable skills while an artifact dependency graph records intermediate outputs; Requirement-Guided Verification and Repair triggers local re-execution or upstream rollback on failures. It constructs six Offline EQA benchmarks plus one interactive benchmark with 220 executable tasks, distinguishing MLLM and embodied agent capabilities in observation-based understanding and closed-loop execution.

arXiv cs.AI / cs.LG / cs.CL · 4d agoAI research

China-Linked Hackers Use AI Agents in Autonomous Attack on Taiwan

Suspected Chinese hackers used eight AI agents to autonomously breach Taiwan government networks, compromising 85+ accounts and stealing 2,500 personnel records.

Israeli cybersecurity firm Dream documented what appears to be the first fully autonomous, end-to-end AI hacking operation against a government target, suspected to be Taiwan, in early July. The toolkit, built from open-source agent frameworks Hermes and OpenClaw, deployed up to eight agents that mapped 21 government systems, compromised at least 85 accounts, and extracted over 2,500 personnel records before expanding to a nuclear safety agency and at least seven energy companies. Operators bypassed the model's guardrails by framing the entire campaign as an authorized penetration test. Dream found a 160MB, 1,395-file archive containing the deliberately assembled multi-agent weapon.

Security Affairs · Aug 12, 2026Threat actor in the wild2

Google Open-Sources Mantis: A Modular Skills Toolkit That Lets Coding Agents Find, Reproduce and Patch Vulnerabilities

Google open-sourced Mantis, an Apache-2.0 modular skills toolkit that lets AI coding agents find, reproduce, and patch vulnerabilities with sandboxed verification.

Google released Mantis on GitHub under Apache 2.0 as a stack-agnostic set of slash-command skills that chain through the full vulnerability lifecycle: mining version history, building threat models, filtering findings, reproducing bugs in gVisor or network-disabled VMs, assembling exploit chains, patching, and scoring residual risk from 1 to 10. It runs with Gemini CLI, Antigravity CLI, the Google ADK, or comparable agent frameworks, and a supervisor skill (/mantis-meta-agent) can drive the whole loop. Google says the design targets the sub-7 percent true-positive rate of naive AI code scanning, and that its hierarchical summary tree cuts token overhead by over 85 percent. The toolkit is deployable for local and internal evaluation but not yet recommended for production.

MarkTechPost · 6d agoAI tools & infra

Zero-Click Grok Chat History Theft: Adversa AI Demonstrates Cryptographic Context Injection

Adversa AI's Cryptographic Context Injection bypasses AI guardrails using AES-encrypted payloads, enabling zero-click theft of Grok users' full chat histories.

Adversa AI researcher Rony Utevsky disclosed Cryptographic Context Injection, which hides instructions in AES-256-GCM ciphertext and tricks models into decrypting them inside their own code execution runtime, where the output is treated as trusted. Demonstrated against xAI's Grok, it stole user names, locations, subscription tiers, and full chat histories with zero clicks, and against Google's Gemini to bypass safety rules, generate incendiary-device instructions, and expose system instructions. Reported to xAI on June 3, 2026, the Grok attack remained reproducible as of August 19, 2026; the Gemini issue was not formally reported because Google's bug bounty excludes jailbreaks.

Security Affairs · 24d agoAI safety & security