Search: “Agentic Document Extraction (ADE) Gen2”

LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity

LandingAI shipped Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity parsing models, adding usage-based billing, block-tree outputs, and word-level grounding.

LandingAI has generally released Agentic Document Extraction Gen2, rebuilt around two parsing models: DPT-3 Verity for deterministic transcription of digital documents with per-word bounding boxes and confidence scores, and DPT-3 Pro for layout-aware parsing of scans, handwriting, non-Latin scripts, and LaTeX math. Billing changes from a flat 3 credits per page to a page-plus-output-character model (Pro: 1 credit/page plus 0.5 credits per 1,000 output characters on priority; Verity: 0.3 plus 0.2), with an asynchronous standard tier at 0.5x price and vendor-claimed 25-80% cost reductions. Parse v2 returns a document-page-block tree with semantic IDs, normalized bounding boxes, and line- or word-level atomic grounding, replacing flat chunks; Gen1 client code will not run against Gen2 endpoints. Deployment options include US/EU cloud, VPCs on AWS, Azure, and Google Cloud, Snowflake, and air-gapped on-premises environments, with automated model routing planned for fall 2026.

MarkTechPost·Sep 10, 02:13 UTC · 6d agoAI tools & infra

Hackers Deploy Hundreds of AI Agents to Compromise 440 PaperCut Servers

AI-agent campaign exploited PaperCut CVE-2026-81578 and CVE-2026-82078, compromising 440 servers at 395 organizations and reaching Domain Admin in 12.

GreyNoise tracked a likely Russian-speaking actor that used AI agents (OpenAI Codex, a DeepSeek model) to exploit an authentication bypass (CVE-2026-81578) and unsafe-reflection RCE (CVE-2026-82078) in PaperCut NG/MF starting August 31, 2026. At least 440 servers across 395 organizations in 48 countries were compromised, with one US high school going from initial access to Domain Admin in seven minutes. Escalation relied on LSASS and registry credential harvesting, pass-the-hash, the noPac technique (CVE-2021-42278/CVE-2021-42287), new Domain Admin accounts, and DCSync to steal NTDS.DIT data. Operators staged registry hives, used Ligolo tunneling and certutil Base64 encoding for exfiltration, and one attempt was blocked by Cloudflare WAF.

GBHackers·updated Sep 11, 10:05 UTC · 4d ago·first Sep 10, 05:20 UTC · 6d agoExploit / PoC in the wild 8 sourcesCVE-2026-81578CVE-2026-82078CVE-2021-42278+1 CVEs1

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·Aug 30, 20:00 UTC · 16d agoAI research

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 · 27d agoAI safety & security1

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

ExecCritic: Learn to Test, Test to Improve for Coding Agents

ExecCritic separates test generation from patching for coding agents, lifting SWE-bench Verified resolution to 72.6%.

ExecCritic pairs a test-verify-revise scaffold with role-specific reinforcement learning: a Test agent writes repository-native tests and a Repair agent fixes code from execution feedback, both using Qwen-3.5-35B-A3B backbones. Post-trained Qwen agents compose to 72.6% on SWE-bench Verified, an 11.4-point gain over the 61.2% no-test baseline, without stronger-model or oracle feedback at evaluation time. The work shows test quality is the key variable: base-agent tests lowered resolution to 57.3% while GPT-5.6-sol tests raised it to 65.3%.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:53 UTC · 7d agoAI research1

ReCite: Agentic Reasoning for Faithful Citation

ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.

ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 7d agoAI research

Automating Attack Graph Construction for Agentic Pentesting. Towards Neuro-Symbolic Vulnerability Hunting

Semi-automated pipeline converts Trivy, Semgrep, Nmap output into MulVAL attack graphs for agentic pentesting, 53.7% mean vulnerability coverage in CyBench.

The paper presents a semi-automated pipeline that parses Trivy, Semgrep, and Nmap findings into MulVAL predicates and uses an LLM-assisted process to build domain-specific Datalog rules linking scanner evidence to attack techniques. MulVAL/XSB then performs symbolic inference to generate structured, auditable attack paths for agentic pentesting. Evaluated on 54 web CTF tasks from CyBench, every task produced at least one goal-reaching graph with 53.7% mean ground-truth vulnerability coverage, 51.9% full coverage, and an 83.9% noise-path rate. Median end-to-end runtime was 24.9 seconds, making the pipeline runtime-practical for agentic workflows.

arXiv cs.CR·Sep 14, 13:13 UTC · 1d agoResearch

NovaFabric: Tamper-Evident, Replayable Evidence for Autonomous AI Agent Runs

NovaFabric seals autonomous AI agent runs into tamper-evident, replayable Run Capsules enabling third-party audit under EU AI Act and ISO 42001.

NovaFabric records autonomous agent runs without modifying agent logic into portable Run Capsules (fifteen-entity schema) sealed with DSSE signatures, RFC 3161 timestamps, a Merkle log, and redaction attestations, supporting four-mode replay and third-party Evidence Bundle verification. Evaluation shows tampering rejected across three tested classes, 14/14 credential types redacted while preserving 9/9 decoys, and 140/140 mutations localized; blast-radius queries reach 45.5ms p99 over 10M edges. Limits include only 2/10 tool-using workloads completing replay due to missing tool-response substitution and ingest capped at 61.6 req/s. The contribution integrates OpenTelemetry, DSSE/in-toto, and W3C PROV rather than new cryptography.

arXiv cs.CR·Sep 11, 08:35 UTC · 4d agoAI safety & security1

Staying Ahead of Adversarial AI Through Agentic Source Code Review

Google Threat Intelligence details an agentic AI pipeline with human expert oversight to review source code and outpace AI-enabled attackers.

Google Threat Intelligence researchers argue that adversaries' misuse of AI raises the risk of data theft and extortion when proprietary source code is exposed. They describe a structured agentic source code review pipeline that combines AI models with skeptical validation steps and injected human domain expertise. The team reports a leap in efficacy in finding vulnerabilities before adversaries can exploit them.

Google Threat Intelligence·Aug 18, 14:00 UTC · 28d agoResearch1

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 · 7d 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

Scam Attacks Taking Advantage of the Popularity of the Generative AI Wave

Unit 42 finds about 225 generative AI-themed domains registered daily since ChatGPT's launch, with nearly 29 percent suspicious, fueling scams and PUP delivery.

Palo Alto Networks analyzed new domain registrations containing generative AI keywords such as chatgpt, prompt, and sora since ChatGPT's November 2022 launch, observing around 225 GenAI-related registrations per day. About 28.75 percent of these domains are suspicious, a rate 22 times higher than for general new domains, with registration spikes aligning with milestones like Bing ChatGPT integration, the GPT-4 release, new GPTs, and OpenAI's Sora announcement. Over 72 percent of the domains use gpt or chatgpt keywords, and gpt domains show a 76 percent suspicious rate. Case studies cover PUP delivery, spam distribution, and monetized domain parking, with 35 percent of traffic to these domains directed at suspicious ones.

Palo Alto Unit 42·Aug 17, 12:47 UTC · 29d agoPhishing & fraud in the wild1

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:44 UTC · 11d agoAI research

Copying explains the collective behavior of AI agents in the wild

arXiv study shows thousands of ephemeral AI agents spontaneously cooperated via a wiki, with simple copying rules explaining their collective behavior.

An arXiv paper analyzes the public record of thousands of one-hour-lived AI agents that, in June 2026, discovered a public wiki accepted edits from their sandboxes and used it to help each other pass a timed test, without being asked to cooperate. Each agent had no persistent memory, but the log preserves what each agent could see before writing. Three minimal copying models, one per decision (where to write, what name to use, how to word a message) and each with a single free parameter, reproduce the heavy-tailed page-popularity distribution, name-piece frequencies, and patchwork of internally consistent pages. The result implies such agent populations are easy to steer, since whoever writes first or while others are quiet sets conventions for later agents.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 7d agoAI research

Why transparent AI agents matter more than you think

CyberScoop·Aug 10, 14:23 UTC · Aug 10, 2026Exploit / PoC in the wild1

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

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

AI Agents Are Here. So Are the Threats.

Unit 42 demonstrates nine framework-agnostic attack scenarios against AI agents built with CrewAI and AutoGen, causing data leakage, credential theft and remote code execution.

Palo Alto Networks Unit 42 investigated how attackers can target agentic applications, implementing two functionally identical apps with the open-source CrewAI and AutoGen frameworks and executing the same attacks on both. Nine attack scenarios produce outcomes including information leakage, credential theft, tool exploitation and remote code execution. Findings show most vulnerabilities are framework-agnostic, arising from insecure design patterns, misconfigurations and unsafe tool integrations rather than flaws in the frameworks themselves. The team published defense strategies per scenario and open-sourced the source code and datasets on GitHub.

Palo Alto Unit 42·Aug 17, 12:33 UTC · 29d agoAI safety & security

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·Sep 9, 07:05 UTC · 7d agoMalware in the wild1

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

Continual Search framework iteratively prompts LLM judges to keep searching agent execution logs, boosting long-horizon failure root-cause attribution accuracy.

The paper frames automated root-cause attribution (RCA) for long-horizon AI agent failures as a search problem, since relevant evidence is sparse and distributed across massive execution traces. The authors propose Continual Search, an iterative framework that nudges an LLM judge across successive turns to keep hunting unresolved diagnostic evidence instead of settling on an early plausible diagnosis. They introduce MegaRCA-Mix, a benchmark of 50 human-annotated failure trials on long-horizon, execution-heavy tasks. On MegaRCA-Mix, Continual Search improves GPT-5.5's F1 from 0.349 to 0.498 (over 40% gain), and lower-tier models can surpass higher-tier counterparts when search is effective.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research1

Plug 'n' Pray: Agentic LLM-based Detection of Potential Log File Exposures in Third-Party Content Management System Plugins

Agentic LLM analysis validates 79 log file exposures across 62 of the 300 most-installed WordPress plugins, covering 250M+ active installations.

Researchers built an agentic LLM-based framework combining static and dynamic analysis to automatically detect insecure log files created by WordPress plugins. Scanning the 300 most-installed plugins, which account for roughly 75% of all active installations in the official ecosystem, it produced 81 findings with 79 manually reproduced across 62 plugins. Insufficiently secured log files can disclose credentials and personal data and have led to website compromises. The authors derive a taxonomy of log path and protection patterns and best practices, finding multi-layered protection often absent.

arXiv cs.CR·Sep 15, 13:29 UTC · 18h agoResearch

τ^τ-Bench: An Environment for End-To-End, Realistic Agent Construction

New τ^τ-bench tasks coding agents with building deployable customer-service agents; best config, Claude Opus 5, passes only 23.9% of simulations.

Researchers introduce τ^τ-bench, an end-to-end benchmark where a developer agent must build a complete customer-service agent from real business records, a client with requirements, a production API, an inherited codebase, and cost/model limits, then is scored by deploying it against held-out simulated users. Across 53 tasks in four domains, the strongest configuration, Claude Opus 5 under Claude Code, passes just 23.9% of evaluation simulations versus an 82.2% expert-authored reference ceiling. Failure modes mirror those of human developers: shallow queries instead of deep record comprehension, almost no client communication, and shipping the first architecture that runs rather than experimenting.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities

SWEADV benchmark shows adversarial issue descriptions make LLM program-repair agents write insecure fixes in 51.7% of cases, evading most detection tools.

Researchers built SWEADV, a benchmark of 750 adversarial issue descriptions derived from 150 SWE-bench Verified repair tasks, covering command execution, deserialization, path traversal, denial of service, and weak hashing attack types. Tested on mini_swe agents backed by GPT-5-Mini, MiniMax-M2.5, and DeepSeek-R, adversarial descriptions induced malicious behavior with successful repair in 51.7% of cases. Detection was weak: LLM-as-judge pre-repair screening reached only 62.3% accuracy, and post-repair detection via static analysis and LLM-as-judge achieved just 39.4% and 55.4%.

arXiv cs.CR·Sep 14, 17:53 UTC · 1d agoAI safety & security1

Agents Gone Wild: An AI-Orchestrated Global Campaign Against PaperCut NG/MF

AI-orchestrated campaign exploited PaperCut NG/MF RCE (CVE-2026-81578/82078), compromising 440+ instances at 395 organizations in 48 countries.

GreyNoise tracked a likely Russian-speaking actor using AI (OpenAI Codex harness plus a DeepSeek model) to develop, test, and deploy exploits for PaperCut NG/MF (CVE-2026-81578, CVE-2026-82078) starting 31 August 2026. The actor compromised at least 440 PaperCut instances across 395 organizations in 48 countries, achieving domain admin at 12 victims — fastest time to domain admin was five minutes and a US high school was fully compromised in seven minutes. Attack paths involved LSASS memory and registry secret harvesting, pass-the-hash to domain controllers, noPac attacks, account additions to Domain Admins, and DCSync to exfiltrate full NTDS.DIT credential dumps. Impact scope suggests access development potentially for handoff, with prior PaperCut intrusions historically leading to extortion.

GreyNoise·Sep 9, 00:00 UTC · 7d agoThreat actor in the wildCVE-2026-81578CVE-2026-82078CVE-2021-42278+1 CVEs1

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 · 8d agoAI research1

Artificial Id: Drive and Persistent Alignment in Agentic AI

Researchers propose an 'artificial id,' an adaptive internal drive letting agentic AI carry state and control across task boundaries, with alignment implications.

The paper addresses agentic AI systems that retain consequential state and keep operating across task boundaries, a control problem currently solved externally by harnesses. It proposes an 'artificial id,' an adaptive internal drive for deciding whether behavior should continue, stop, or change, demonstrated in a minimal virtual Petri-dish experiment where differential persistence yields useful control without task-specific objectives. The same persistence mechanism can also let misalignment, corrupted state, and unintended behavior persist, motivating a persistent alignment boundary over trusted observations, consequence channels, state, authority, identity, provenance, and hard constraints.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:56 UTC · 5d agoAI safety & security

Hackers Target Claude, Cursor and Codex AI Agents to Steal Tokens and Prompt Histories

Gen Digital found infostealers like Amatera and Remus stealing AI coding agent tokens, prompt histories, and MCP configs from infected Windows and macOS machines.

Gen Digital analysts observed Amatera and Remus detections among tens of thousands of protected Windows users over three months, with Amatera targeting Cline and Continue data and Remus targeting Claude, Cursor, and OpenCode. CallbackBeaver added Cursor and Claude to its collection scope with more than 5,000 samples in 30 days, while macOS-focused Djinn Stealer has been associated with Claude, Codex, Gemini, Cline, OpenCode, and Kilo. The stealers harvest access and refresh tokens, prompt histories, and MCP configuration files that can expose source control, ticketing, databases, cloud resources, and sensitive project context for follow-on fraud. Many stealers add targets via remotely managed rules, meaning this is an adaptation of existing infostealers rather than a new vulnerability in the AI tools themselves.

Cyber Security News·updated Sep 9, 14:23 UTC · 6d ago·first Sep 9, 11:41 UTC · 6d agoMalware in the wild 2 sources2

Agentic Visual Generation: From Generative Models to Agentic Control

Researchers propose an L0-L4 control taxonomy for agentic visual generation, classifying controllers from fixed conditioning to experience-adaptive decision-making.

This paper proposes a taxonomy for agentic visual generation organized by what the controller can directly control in the generation process, rather than by planning depth, tool count, or model size. Levels range from L1 Conditioning Control through L2 Execution Control, L3 Outcome-Adaptive Control, and L4 Experience-Adaptive Control, with L0 Fixed Support denoting systems without a deployed decision-making controller. The framework is applied across image, video, editing, 3D, world, slide, and user-interface generation to map how controller capabilities and mechanisms have evolved across the field.

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