Search: “autoit”

Hackers Abuse AutoIt to Inject AsyncRAT Into Microsoft-Signed Windows Process

Attackers use AutoIt and an invoice-themed batch file to inject AsyncRAT into Microsoft-signed charmap.exe for stealthy remote access.

Point Wild Threat Intelligence documented a five-stage campaign starting with 'Right-click to open Invoice Details.bat' that launches hidden PowerShell, drops a renamed AutoIt interpreter and encrypted loader into a random Temp folder, and persists via a Startup-folder batch file. The loader decrypts AsyncRAT only in memory and injects it into the Microsoft-signed charmap.exe (Windows Character Map) process, leaving no payload file on disk. The final payload patches AMSI inside the infected process and includes screen-capture code that prepares images for transfer to a command-and-control server at 158[.]51[.]122[.]136:4944.

Cyber Security News·Sep 14, 07:07 UTC · 2d agoMalware in the wild1

AutoTrans: AI-Assisted Automatic Translation of Security Assertions for RISC-V Processors

AutoTrans uses LLMs with regex extraction and formal verification to automatically translate security assertions across RISC-V processors, achieving 78% unattended acceptance.

AutoTrans is an automated framework for translating verified security assertions between RISC-V processor targets, where manual translation takes hours per assertion. It combines a regex-based SystemVerilog signal extractor to prevent LLM signal hallucination, a pinned prompt template yielding byte-identical prompts resilient to model updates, and JasperGold FPV formal verification of generated assertions. Applied with DeepSeek V4 to translate assertions between RISC-V targets such as IBEX and NS31A, it achieves a 78% automatic translation acceptance rate without human intervention and 100% after human refinement.

arXiv cs.CR·Sep 9, 11:31 UTC · 7d agoResearch

AsyncRAT Malware Abuses AutoIt and PowerShell to Hide Inside Legitimate Windows Process

Point Wild details a five-stage AsyncRAT campaign using AutoIt abuse and process injection to hide a .NET RAT inside Microsoft's signed charmap.exe.

Researchers at Point Wild Threat Intelligence analyzed a campaign starting from a lure batch file ('Right-click to open Invoice Details.bat') that launches hidden PowerShell to reconstruct a Base64/XOR-obfuscated payload. The stage drops a renamed signed AutoIt interpreter and loader script into %LOCALAPPDATA%\Temp and achieves persistence via a batch file in the user's Startup folder (T1547.001). The loader decrypts the payload in memory with single-byte XOR key 0x36 and injects it into SysWOW64\charmap.exe using OpenProcess/VirtualAllocEx/WriteProcessMemory/CreateRemoteThread, with PE-sieve confirming an in-memory implanted PE and patched AMSI modules. The final AsyncRAT payload performs screen capture and data theft, with observed C2 at 158[.]51[.]122[.]136:4944 over raw TCP.

GBHackers·Sep 14, 05:24 UTC · 2d agoMalware3· 1 read

Identifying Agentic Automation with Behavioral Telemetry

Akamai describes detecting autonomous AI browser agents like Comet using Masked Autoencoder Transformer models on sparse behavioral telemetry.

Akamai researchers present a behavioral telemetry approach for identifying agentic automation in web traffic. Masked Autoencoder Transformer models are used to detect the sparse behavioral signals produced by autonomous AI browser agents such as Comet. The work targets traffic classification and bot detection rather than a specific vulnerability, and becomes more relevant as agentic browsing adoption grows.

Akamai Blog·Aug 19, 19:00 UTC · 28d agoAI safety & security1

Hackers Deploy Casbaneiro Banking Trojan That Activates When Victims Open Bank Websites

Fortinet details Casbaneiro banking Trojan campaign hitting Latin American bank customers via invoice-themed PDF phishing, with AutoIt loading, RegSvcs.exe injection, and bank-site-triggered activation.

Fortinet identified an August 2026 Casbaneiro banking Trojan campaign targeting users in Argentina, Peru, Colombia, and Mexico through phishing PDFs styled as urgent invoices or legal notices. The staged chain uses IP-based geo-filtering, a Base64-encoded ZIP, an HTA file, and a legitimate AutoIt interpreter before injecting into RegSvcs.exe or mobsync.exe and persisting via a Startup shortcut. The Trojan exfiltrates address book and Outlook data unencrypted, stays dormant until victims visit targeted bank sites, then accepts commands for keyboard control, clipboard pasting, file execution, and command execution. It uses an expected HTTP 403 response from a second server and malformed HTTP requests to complicate network analysis.

Cyber Security News·Sep 14, 08:42 UTC · 2d agoMalware in the wild

Automox Mitigation Worklets cut endpoint exposure to unpatchable flaws

Automox launched an AI-speed Mitigation Worklet Pipeline that drafts and publishes mitigations for unpatchable vulnerabilities within hours of disclosure.

Automox announced its Mitigation Worklet Pipeline, which uses AI to draft mitigations for unpatchable vulnerabilities and publishes human-reviewed Worklets to its catalog within hours of disclosure. The company cites rising vulnerability volume, including a record Patch Tuesday with 973 CVEs, as motivation. Customers can search Worklets by CVE, control deployment targets, and verify execution through Activity Logs and Policy Results.

Help Net Security·Sep 11, 07:48 UTC · 5d agoTools

Casbaneiro Banking Trojan Uses Distributed C2 Servers to Evade Detection and Target Bank Users

Casbaneiro banking Trojan targets Latin American bank users via phishing PDFs, geofencing, AutoIt staging, and distributed C2 servers to evade detection.

FortiGuard Labs detailed a Casbaneiro banking Trojan campaign observed in August 2026 targeting users in Argentina, Peru, Colombia, and Mexico via fake invoice and legal-notice emails. The operation uses geofenced landing pages, an HTA downloader with WMI-based sandbox checks, a legitimate AutoIt interpreter, and process injection into RegSvcs.exe (fallback mobsync.exe). C2 activation is conditional on victims visiting banking websites, enabling keyboard control, clipboard manipulation, and fake overlay windows for bank-focused fraud, while distributed exfiltration servers and unusual HTTP 403-based signaling hinder analyst correlation.

GBHackers·Sep 14, 05:56 UTC · 2d agoMalware in the wild1

Anthropic to put AI in charge of reviewing Claude Code actions by default

Help Net Security·Aug 10, 00:00 UTC · Aug 10, 2026Vulnerability1

New Python-Based Payload MechaFlounder Used by Chafer

Unit 42 identified MechaFlounder, a new Python backdoor used by the Chafer group to target a Turkish government entity via HTTP-based C2.

Unit 42 reports that in November 2018 the Chafer group targeted a Turkish government entity, reusing the win10-update[.]com infrastructure reported earlier in 2018 by ClearSky. The new secondary payload, tracked as MechaFlounder, is Python compiled with PyInstaller and acts as a backdoor supporting file upload/download and command execution. It beacons over HTTP using anomalous GET requests containing the username and hostname, suggesting a custom C2 server rather than a standard web server. Code overlap with OilRig's Clayside VBScript was noted, but Chafer and OilRig remain tracked as separate groups.

Palo Alto Unit 42·Aug 17, 13:22 UTC · Aug 17, 2026Threat actor

Companies Have 6 Months to Prepare for Automated Attacks

Dark Reading warns that frontier AI models have demonstrated autonomous end-to-end compromises and urges companies to prepare for AI-driven automated attacks within six months.

This analysis piece argues that frontier AI models can already autonomously, and sometimes inadvertently, carry out end-to-end system compromises. It frames AI-driven automated attacks as a near-term operational risk that defenders have roughly six months to prepare for. No specific incident, vendor, or technique is disclosed in the source text.

Dark Reading·Sep 4, 15:57 UTC · 12d agoAI safety & security

Exploring the Latest Mispadu Stealer Variant

Unit 42 found a new Mispadu infostealer variant targeting Mexican users via malicious .url files exploiting the SmartScreen CVE-2023-36025 bypass.

Unit 42 discovered a new variant of Mispadu Stealer, a Delphi-based banking trojan first reported in 2019, found while hunting for the Windows SmartScreen bypass CVE-2023-36025. The campaign uses crafted .url files referencing UNC network-share paths with an HTTP port (@80) that forces payload retrieval over WebDAV via rundll32.exe, avoiding SmartScreen warnings. Analyzed samples (~4 KB, compiled 2023-11-12) predate the CVE publication, and ZIP payloads were likely distributed as email attachments, primarily targeting users in Mexico.

Palo Alto Unit 42·Aug 17, 11:46 UTC · Aug 17, 2026Malware in the wildCVE-2023-360251

Webinar Today: Keep Pace With AI – A New Operating Model for Endpoint Remediation

SecurityWeek and Automox host a webinar on accelerating endpoint vulnerability remediation through automation and governance policies.

The 20-minute webinar promotes 'Frontier Pace Governance,' an approach to balancing automation, policy, and business risk in endpoint patching. It is vendor marketing content co-hosted with Automox, covering visibility, remediation automation, and patching SLAs.

SecurityWeek·Sep 10, 13:30 UTC · 6d agoIndustry

Guildma (Astaroth) malware infection from Brazilian Portuguese email, (Tue, Sep 1st)

Guildma (Astaroth) malware spread via geofenced Brazilian Portuguese phishing emails, using zip archives, shortcuts, and AutoIt loaders.

SANS researchers documented a Guildma (Astaroth) malware infection initiated on 2026-08-31 by a malicious Brazilian Portuguese email offering a DocuSign-themed PDF. The delivery link is geofenced to Brazil and requires Brazilian Portuguese browser and OS language settings, otherwise serving a legitimate installer. The infection chain uses a zip archive containing a Windows shortcut that retrieves a DLL saved as an alternate data stream, which installs a compiled AutoIt package for persistence. Unique SHA-256 indicators and HTTPS traffic to Azure-hosted and .cfd domains were observed, with prior reports noting the campaign's abuse of GitHub.

SANS Internet Storm Center·Sep 1, 21:30 UTC · 14d agoMalware in the wild

Identifying Agentic Automation with Behavioral Telemetry: Part 2

Akamai details behavioral telemetry signals for identifying agentic automation traffic in the second part of its research series.

Akamai published part two of its series on identifying agentic automation using behavioral telemetry. The post appears on Akamai's security research blog and focuses on detecting AI-agent-driven traffic. Full article text was unavailable at classification time, so classification relies on the title and source.

Akamai Blog·Aug 27, 12:00 UTC · 20d agoResearch

ThreatsDay: 296K IoT Botnet, 100+ Water Systems Targeted, SharePoint RCE Chain + 27 New Stories

Weekly ThreatsDay bulletin details a ShinyHunters-style social engineering hit on ReliaQuest, the 296,000-device Dysphoria IoT botnet, and several new malware families.

ReliaQuest confirmed a social engineering attack on August 22, 2026, in which an attacker used a fake SSO page and MFA push approval to gain brief view-only access to an identity dashboard, with tactics matching ShinyHunters, which has since listed the firm on its leak portal. The Shadowserver Foundation reported the Dysphoria botnet has compromised nearly 296,000 IoT devices for DDoS attacks and recently added residential proxy capability. Cisco Talos documented JWR, an operator-driven phishing-as-a-service framework linked to The Outsider that harvests credentials, identity documents, and 2FA codes over an encrypted WebSocket. New malware coverage includes the Octagon Android fraud bot ($1,400/month), the C2Looper Rust backdoor delivered via ClickFix, and the Aeternum loader that moved C2 to the Polygon blockchain.

The Hacker News·Sep 1, 17:35 UTC · 15d agoMalware in the wild

LLM Agents as Computational Typologists

AUTOTYPOLOGIST is an LLM agent that performs evidence-grounded linguistic typology analysis over 25 open-source reference grammars.

The agent retrieves relevant grammar sections, analyzes interlinear glossed text (IGT), and iteratively reasons over typological hypotheses in a ReAct-style workflow. It was evaluated on typological feature coding against expert annotations and hypothesis testing against universals using 25 open-source reference grammars. Results suggest LLM agents can support scalable, inspectable crosslinguistic analysis but still require expert validation.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:35 UTC · 9d agoAI research1

The Evolution of the Agent Harness

Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.

The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.

Latent Space·Aug 22, 07:30 UTC · 25d agoAI tools & infra

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled autoregressive testbed shows validation losses must be analyzed per task, and image tokenizer choice affects joint multimodal text modeling.

Researchers built a pure-autoregressive testbed to study image tokenizers as the 'visual language' of unified multimodal models, tracking task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They found that losses exhibit distinct scaling behavior per task and rank tokenizers differently, and that I2T loss over a shared text vocabulary gives a more consistent loss–performance signal than T2I loss. Better reconstruction does not necessarily yield lower task-specific losses or stronger downstream performance, and tokenizer choice can affect text modeling under joint optimization. Case studies examine the discriminator, semantic supervision, and vocabulary size design axes.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

Agent as Policy for Robotic Manipulation

Agent as Policy lets a general-purpose agent drive a physical robot via runtime reasoning and program generation, reaching 100% success on manipulation tasks.

The paper introduces Agent as Policy (AGP), which puts task planning and execution for a physical robot under a general-purpose agent's control with no task-specific or environment-specific training. The agent interprets visual evidence, writes executable programs, issues motion commands, and revises actions based on physical outcomes. AGP was evaluated on real-world manipulation tasks including assembly from human videos, block construction from goal images, die reorientation, targeted throwing, and bimanual towel folding. It achieved success rates of 100%, 100%, and 80% on three block construction configurations.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

Beyond Solver Verdicts: Generative Reward Models for Autoformalization

Researchers introduce Generative Verification (GenV), a generative reward model achieving 0.961 AUROC in detecting unfaithful autoformalization that preserves solver verdicts.

The paper formalizes Verdict-Preserving-Unfaithfulness (VPU), a failure mode in neurosymbolic autoformalization where an incorrect encoding executes successfully and matches the expected solver verdict, and proves verdict-only verification is bounded to chance-level detection. The proposed Generative Verification (GenV) distills an offline Z3-equivalence oracle into a reference-free, continuous reference-equivalence score within the language model's vocabulary space. The oracle-mined verifier (GenV+HN) achieves 0.961 AUROC, generalizes zero-shot across unseen translators and formal styles, and yields an 11.3-point downstream accuracy gain in agentic test-time compute allocation. Mechanistic analysis with decision-projected logit lenses and sparse autoencoders shows the generative readout extracts precise spatial error coordinates without explicit localization training.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research1

terms.txt: A Consent and Compensation Protocol for Agentic Web Access

terms.txt specifies a robots.txt-style protocol for per-path, per-purpose AI crawler consent and compensation, with enforcement adding 0.20-0.65 ms per request.

The paper documents that automated clients now make up most web requests, that training dominates Cloudflare-classified crawling, and that the largest AI platforms fetch thousands of pages per returned visitor while robots.txt cannot express identity, purpose, terms, or price. It specifies terms.txt plus an origin-enforced exchange using Web Bot Auth signatures, signed intent, delegation tokens, HTTP 402 negotiation, and signed receipts. A dependency-free implementation adds 0.20 to 0.65 ms per request on one vCPU.

arXiv cs.CR·Sep 10, 07:01 UTC · 6d agoResearch

Researchers Turn USB Auto-Install Into a Full SYSTEM Takeover on Windows 11

DEF CON researchers chained Windows Plug and Play auto-install abuse with third-party driver flaws to reach SYSTEM on fully updated Windows 11.

In 'Plug And Pwn' research prepared for DEF CON 34, Alejandro Hernando and Borja Martinez emulated USB devices so Windows auto-installed signed vendor software, chaining a Sierra Wireless SwiService.exe SetDNS primitive, a Sony FeliCa co-installer path-traversal flaw that writes a DLL into System32, and device reconnection for SYSTEM code execution. A remote variant over Remote Desktop forges a synthetic Intel RealSense device and abuses a CRYPTBASE.dll search-order hijack when PnP or low-level USB redirection is enabled. Microsoft notes RDP does not allow supported PnP and RemoteFX USB redirection by default, and no in-the-wild exploitation has been reported.

The Hacker News·Aug 11, 10:48 UTC · Aug 11, 2026Research

Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

Case study shows autonomous LLM research reaches 90% of SOTA on telecom ticket retrieval in 10 weeks versus 10 months human work.

The paper explores adapting autonomous research to open-ended, industry-grade ML problems through a telecom ticket retrieval case study with commercial and open-source agents. Autonomous research reached 90% of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in 10 weeks versus 10 months of human work, at up to $200 per Cursor campaign. The authors find agents excel at narrow hyperparameter optimization but lack human-like intuition, recommending human-agent collaboration.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:09 UTC · 5d agoAI research

SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?

SAEScientist-Bench tests whether AI agents can autonomously run SAE interpretability research on Gemma-2-9B-IT; frontier agents trail expert baselines.

The benchmark requires agents to design contrastive probes and navigate the Gemma Scope dictionary of over 131K features in Gemma-2-9B-IT to discover optimal interpretable features, scored against expert-curated references on Neuronpedia via activation rank, concept selectivity, and causal steering. Across 10 agent configurations and 20 tasks, frontier agents demonstrate genuine discovery capability and approach expert levels at separating target concepts from controls, but lag substantially in causal steering and frequently misinterpret experimental measurements. The authors frame this as establishing experimental model understanding as a measurable capability for closed-loop autonomous AI R&D and post-hoc monitoring for recursive self-improvement.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:45 UTC · 8d agoAI research1

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:57 UTC · 8d agoAI research1

[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over

Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.

Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.

Latent Space·Aug 22, 07:36 UTC · 25d agoAI industry

ActionSplice: In-Flight Action Editing for Interactive World Models

ActionSplice enables in-flight action editing in chunk-autoregressive video world models via a lightweight corrector, avoiding rollback or waiting for the next chunk.

ActionSplice is an inference framework that formulates in-flight action editing for chunk-autoregressive video world models as Counterfactual State Transport (CST), where a lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. Across minWM-Wan Action2V and HY-WM1.5, the retargeting variant CST-R reduces rollback-relative LPIPS by 61.5% and 75.9% versus direct condition swapping, while the temporal-splicing variant CST-T reduces suffix LPIPS by 56.1% and 77.5% with 2.73x and 1.69x pixel-ready speedups over waiting.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoModel release2

Opaque recurrence, and other AI terms that you should probably know

TechCrunch updates its plain-English glossary defining common AI terms from AGI and agents to chain-of-thought reasoning.

TechCrunch maintains a regularly updated glossary of AI terminology, defining terms such as AGI, AI agents, API endpoints, chain of thought, coding agents, compute, deep learning, and diffusion. It highlights 'opaque recurrence', the reasoning technique in OpenAI's new Astra model that has drawn attention from AI safety researchers. The piece is an educational living document rather than new research or a product announcement.

TechCrunch · AI·Sep 7, 19:24 UTC · 9d agoAI industry1

AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems

AgentGrad introduces intervention-guided prompt optimization for LLM multi-agent systems, achieving state-of-the-art results with 2.5x faster optimization.

AgentGrad is a prompt optimization framework for LLM-based multi-agent systems that addresses limitations in textual gradient extraction and aggregation. It uses sequential intervention to identify the agent whose prompt modification resolves a given failure, then applies agent-level supervision and semantic gradient clustering to build generalized gradients. Experiments report state-of-the-art performance across five MAS benchmarks and a 2.5x average reduction in wall-clock optimization time versus the next-fastest baseline.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

Import AI 468: 23 RSI ideas; PostTrainBench+; and how trust and transparency interplay with AI racing

Import AI covers 23 IFP policy ideas for automated AI R&D risks and MIT/Columbia's game theory of AI racing slowdowns.

Think tank IFP published 23 policy recommendations across seven categories to help policymakers address risks from increasingly automated AI R&D. MIT and Columbia researchers released 'Racing to Ruin,' a game theory model showing that coordinated slowdowns between rival AI firms hinge on trust and transparency. The newsletter also links a short story on interacting with powerful AI systems.

Import AI·Aug 10, 12:32 UTC · Aug 10, 2026AI research

I wrote an AI textbook — how long until AI can do it better?

AI researcher Nathan Lambert argues LLMs remain weak at long-form technical writing, questioning whether models can autonomously organize scientific knowledge for breakthroughs.

Nathan Lambert describes writing a post-training textbook, Reinforcement Learning from Human Feedback, and finds today's LLMs weak at organizing long-form technical content despite becoming superhuman at coding and math. He notes GPT 5.5 Pro found deep typos across a 200-300 page manuscript while Claude models proved more useful as editors. He argues that compressing knowledge through writing is a prerequisite for autonomous scientific insight and tempers expectations for near-term AI-driven open science.

Interconnects·Aug 12, 13:01 UTC · Aug 12, 2026AI research

Governing Bring Your Own AI: A Parameterized Maturity Model

Researchers propose a parameterized governance model and maturity ladder for Bring Your Own AI, finding data exposure and compliance dominate BYOAI risks.

The paper studies Bring Your Own AI (BYOAI), where employees use personal generative AI accounts such as ChatGPT, Gemini, and Claude outside enterprise identity and security controls. Drawing on a curated corpus of 30 records (24 studies and 6 framework documents), the authors build a risk taxonomy, a five-level governance maturity ladder, and a parameterized model linking control-layer coverage to residual risk. Findings highlight data exposure and compliance as the most prominent risks, inconsistent framework engagement, and evidence that layered technical controls reduce modeled exfiltration risk more than prohibition-based approaches.

arXiv cs.CR·Sep 4, 15:03 UTC · 12d agoResearch

From assistance to execution: How enterprises put AI to work

OpenAI reports enterprise adoption patterns of agentic AI through ChatGPT and Codex, with frontier firms pulling ahead in deployment.

OpenAI published a report on how enterprises are moving from AI assistance to autonomous execution, focusing on agentic AI deployments. It highlights the use of ChatGPT and Codex in enterprise workflows and argues that frontier firms are pulling ahead of peers in AI adoption. The piece is adoption research and marketing rather than a security disclosure.

OpenAI News·Aug 12, 06:00 UTC · Aug 12, 2026AI industry1

Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.

The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.

MarkTechPost·Sep 13, 05:56 UTC · 3d agoAI research1

SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?

SAEScientist-Bench evaluates whether AI agents can autonomously conduct SAE interpretability research in Gemma-2-9B-IT, finding frontier agents trail expert baselines.

SAEScientist-Bench tests if AI agents can act as scientists using SAE tools for autonomous mechanistic discovery, requiring them to design contrastive probes and navigate a Gemma Scope dictionary of 131K+ features in Gemma-2-9B-IT. Across 10 agent configurations and 20 tasks, frontier agents showed genuine discovery capability but remained well behind expert reference features, lagging most in causal steering. Agents frequently misinterpreted experimental measurements even when designing effective contrasts.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness

NeoHorse-1 introduces agentic post-training with intelligent routing that lifts agent benchmark scores at 4B and 9B scales, prototyping recursive self-improvement.

NeoHorse-1 is a family of agent-native models trained through agentic post-training: routing-harness logs (predicted capability demand, service tier, interaction) become structurally validated training data organized into a three-stage SFT curriculum plus routing-guided on-policy distillation. Capability-guided allocation converts evaluation feedback into the next training mixture, closing an evaluation-selection-update loop. Post-training raises the macro-average from 58.94 to 64.87 at 4B and from 65.60 to 69.04 at 9B across eleven agent, tool-use, coding, and instruction-following benchmarks. The authors position it as a prototype of harness-mediated recursive self-improvement.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

Who Should Grade My Work? Student Perspectives on Transparent AI-Assisted Writing Assessment in Higher Education

A Saudi university study finds students value ChatGPT writing feedback but treat human instructors as the final grading authority.

Thirteen male undergraduate computing students at a Saudi public university completed handwritten writing tasks that were scored by ChatGPT using a rubric-based prompt, then reflected after being told the score and feedback were AI-generated. Inductive thematic analysis identified four themes: perceived feedback usefulness, awareness of AI's contextual and pedagogical limitations, conditional trust, and reflection on the instructor's institutional role. Participants accepted GenAI feedback for surface-level revision but consistently positioned human instructors as the authority over grading decisions, distinguishing feedback utility from evaluative authority.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:49 UTC · 12d agoAI research