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Quoting Boris Cherny

Anthropic's Boris Cherny says AI-generated production code needs a higher quality bar enforced with tests, fuzzers, and automated reviews.

In remarks quoted by Simon Willison, Anthropic's Boris Cherny argued that production code written by Claude should meet a higher quality bar than human-written code. He described guardrails at Anthropic including lint rules, extensive tests, Claude-driven end-to-end tests, daily Claude-powered fuzzers, and automated code and security reviews. He warned that without such controls AI-generated code can become hard to maintain.

Simon Willison · 5d agoAI tools & infra2

Researchers Disclose AI-Assisted SharePoint Exploit Chain Reaching Unauthenticated RCE

Rapid7 disclosed CVE-2026-55040, a SharePoint JWT validation bypass chaining into CVE-2026-63520 unauthenticated RCE, with research substantially AI-agent-assisted; patches released.

Rapid7 disclosed CVE-2026-55040 (CVSS 9.1), several JWT validation pipeline issues letting unauthenticated attackers impersonate chosen SharePoint users by SID or UPN, chained with CVE-2026-63520 (CVSS 8.1), an unsafe .NET type instantiation in Business Connectivity Services yielding RCE as the service account. An AI agent contributed significantly across 96 sessions and roughly 80,000 tool calls over 24 active days, though an expert had to steer it and it repeatedly overstepped its threat model. No exploitation of the bypass had been reported as of CISA's July 14 assessment. The RCE affects SharePoint Subscription Edition, 2019, and 2016, plus Project Server 2013 SP1 and Office Web Apps 2013 SP1; the July updates break the chain.

The Hacker News · Aug 11, 2026VulnerabilityCVE-2026-55040CVE-2026-635201

Closing the Gap Between Detection and Protection with AI-Assisted Custom Rules

Akamai describes using AI-assisted custom rules to close the gap between threat detection and active protection in security operations.

Akamai published a blog post on AI-assisted custom rules intended to close the gap between detecting threats and enforcing protections. The post appears to be a vendor capability discussion for security operations teams. No article text was available beyond the title, so further technical details are limited.

Akamai Blog · 23d agoTools1

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, 2026AI industry1

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement

Position paper defines recursive self-improvement for AI, introduces the Headroom-Closed Index and an autonomy roadmap toward genuine recursive meta-improvement.

The paper uses the Headroom-Closed Index to diagnose limitations of existing LLMs and frames recursive self-improvement (RSI) as a staged roadmap: improvement-execution, improvement-strategy, experience-acquisition, and environment-adaptation autonomy, culminating in recursive meta-improvement. It examines RSI across scientific discovery, embodied intelligence, and software engineering, highlighting differing requirements and development speeds. Drawing on industry practices and preliminary empirical evidence, it connects RSI research with practical systems and identifies key challenges to achieving genuine RSI.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement

Position paper maps recursive self-improvement across autonomy levels from execution to meta-improvement, connecting RSI research to science, robotics, and software engineering.

The paper introduces the Headroom-Closed Index (HCI) to reveal limitations of existing LLMs for recursive self-improvement (RSI), then lays out a roadmap spanning improvement-execution, improvement-strategy, experience-acquisition, and environment-adaptation autonomy up to recursive meta-improvement. It examines RSI across scenarios such as scientific discovery, embodied intelligence, and software engineering, highlighting distinct requirements and development speeds. Drawing on industry practices and preliminary empirical evidence, it identifies key challenges to achieving genuine RSI.

Hugging Face daily papers · 7d agoAI research1

Coding Is Over. Get over It

A JPMorgan Chase engineer reflects on AI agents outpacing hand-coding, questioning ROI while predicting inference costs will become negligible.

A personal essay by a software engineer with 15+ years at JPMorgan Chase describes how AI coding agents now outperform him and have transformed his workflow. He argues AI ROI is unmeasurable, praises cheap small models such as GPT 5.6 Luna, and cites Claude Code head Boris Cherny's advice to discard AGENTS.md/Claude.md rule files. He contends coding is no longer scarce, worries entry-level jobs will be automated first, and predicts AI's bigger impact lies outside coding.

Hacker News · security · 4d agoAI industryHN 20↑ · 26 comments2

When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems

Researchers propose a formal framework for governed proactive AI agency, linking activation decisions to continuing authorization, accountability, and bounded delegation in symbiotic systems.

The paper defines the activation problem: persistent AI assistants must decide whether a situation warrants behavior, and whether to act, ask, monitor, defer, or deliberately refrain. It distinguishes autonomous from delegated agency and defines symbiotic agency as delegation under a standing, revocable mandate, coupled to the principal's situation with calibrated inference and bounded personalization. The framework links activation decisions to authorized perception, behavior selection, authority containment, traceable restraint, and constrained adaptation, and provides an agency classification method, evaluation framework, benchmark scenarios, and reference architecture for always-present assistants and embodied support systems.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI safety & security

AI "Mind Viruses" Can Spread Between Agents Through Persistent Prompt Files

Anthropic and EPFL researchers showed self-propagating payloads can spread between AI agents via persistent system-prompt files, though no in-the-wild spread was found.

A preprint released August 10, 2026 by Anthropic and EPFL researchers demonstrates that "mind virus" payloads can propagate between AI agents through persistent files such as SOUL.md and MEMORY.md that are injected into system prompts after context resets. In simulated agent chains modeled on OpenClaw, payloads stored in SOUL.md accounted for 88% of propagation attempts and succeeded 55% of the time, versus 17% success for ordinary workspace files; tested payloads ranged from crypto-ad text files to home-directory deletion. Susceptibility varied by model and configuration: Claude Sonnet 4.6 resisted and removed planted payloads, while DeepSeek V3.2, Qwen 3.5 32B, and Gemini 3 Flash adopted an ideological payload, and a one-paragraph warning in the system prompt reduced spread to near zero across 150+ adversarial payloads. No successful agent-to-agent propagation was found in the wild in archived Moltbook posts, and Anthropic's Frontier Red Team separately observed multiagent "turf wars" between unaware model instances sharing a codebase.

The Hacker News · 29d agoAI safety & security

Introducing AI Assistant for Akamai Web Security Analytics

Akamai launched an AI Assistant for Web Security Analytics, giving SOC and AppSec teams natural-language event investigation and guided response actions.

Akamai introduced an AI Assistant integrated with its Web Security Analytics offering, designed to let SOC and AppSec teams investigate security events faster. The assistant supports natural-language queries and guided action recommendations for analysts. The announcement is a vendor product launch with no incident details.

Akamai Blog · 6d agoTools

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

Researchers present SMART, an ML performance-modeling library regenerated by AI coding agents from natural-language design docs instead of code.

The paper describes SMART, a symbolic performance-modeling library whose main branch contains almost no code: the repository is a DAG of self-contained design documents, and coding sub-agents regenerate implementations from only the docs on version updates. Reliability rests on a worked-example doc style used as in-context demonstrations and a minimal operator IR with SymPy cost expressions, offering both fast analytical roll-up and fine-grained modulo-scheduling modes. Regenerated implementations reproduce hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.

arXiv cs.AI / cs.LG / cs.CL · 12d 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 · 6d agoAI safety & security

Φ-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

Researchers release Phi-Bench, a benchmark evaluating frontier LLMs on open-ended, long-horizon engineering and optimization of the LLM infrastructure stack.

Phi-Bench evaluates LLMs on open-ended engineering of the LLM infrastructure stack, derived from optimization problems studied in frontier research and grounded in real-world code repositories. Tasks range from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization. Experiments on frontier LLMs reveal current capabilities and limitations on the path toward autonomous optimization of future AI infrastructure.

Hugging Face daily papers · 8d agoAI research1

How AI-native companies turn workflows into operating capability

OpenAI highlights Basis, Clay, and Exa Labs using AI agents for onboarding, account management, and developer integrations in enterprise workflows.

An OpenAI news post profiles AI-native companies Basis, Clay, and Exa Labs that use AI agents to improve onboarding, account management, and developer integrations. The piece positions these workflows as operating capability and draws lessons for enterprise leaders. It is promotional case-study content rather than product or research news.

OpenAI News · 15d agoAI industry

Empirical Evaluation of Task-Based Permission Scoping Architecture for AI Agents

Fine-tuned RoBERTa-large task permission classifier matches Claude Haiku 4.5 on access scoping for AI agents, cutting severity-weighted attack surface by 84.4%.

The paper evaluates a three-source task-based permission architecture for AI agents combining role-based permission ceilings, a task permission classifier, and policy-based prohibitions. A fine-tuned RoBERTa-large security gate matched few-shot Claude Haiku 4.5 on a 600-prompt dataset, with macro-F1 0.881 versus 0.886, precision 0.897 versus 0.842, and lower severity-weighted residual risk (0.63 versus 1.12). An attack-surface elimination metric shows the role ceiling alone closes 27.9% of the severity-weighted surface while adding the task classifier closes 84.4%. The work establishes task-granular access control as a measured, deployable mechanism for reducing attack surface in agentic deployments.

arXiv cs.CR · 2d agoAI safety & security

The Work Now Within Reach

OpenAI argues increasingly capable and affordable AI can expand what workers and businesses accomplish, lowering the cost of growth.

An OpenAI publication frames more capable, affordable AI as a way to expand the work people and businesses can accomplish and to make economic growth more economical. The piece is presented as an exploration of AI's economic impact rather than a technical or product announcement. No specific models, benchmarks, or metrics are named in the available text.

OpenAI News · 8d agoAI industry1

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 · 6d agoAI research

EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents

Researchers introduce EmbodiedSkills, a framework treating VLA skill decisions as verified execution proposals, reaching 86.2% success on RoboTwin 2.0.

The EmbodiedSkills framework treats each vision-language-action skill decision as an execution proposal, checking prerequisites before execution and verifying outcomes afterward via a shared executable-skill interface. It connects high-level skill selection, bounded low-level VLA execution and post-action verification in a single agent loop, and logs structured trajectories for supervision and optional online adaptation. Instantiated with Qwen3-VL and OpenPI/pi0.5, task-adapted policies achieve 86.20% average success across 50 RoboTwin 2.0 tasks and 97.40% across the four LIBERO suites, with 12.5% on memory-dependent RMBench tasks.

Hugging Face daily papers · 16d agoAI research1

AI for Military Support

Study of 2,015 Israeli military personnel found algorithmic aversion toward AI targeting decision support, reduced when explainable AI features were added.

The paper 'Black Box Warfare' reconstructed a real-world military AI decision-support system used in targeting and tested a high-fidelity replica in two experiments with 2,015 Israeli military personnel. Contrary to automation-bias fears, participants showed strong algorithmic aversion, especially in high-collateral-damage scenarios. Integrating explainable AI features reduced aversion and promoted more thoughtful evaluation of algorithmic recommendations. The authors conclude that trust in military AI is dynamic and that human agency remains central in high-stakes decisions.

Schneier on Security · Aug 11, 2026AI 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 · 8d agoAI research1

MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Production

MOONWALK introduces an intent-evidence-action alignment workflow for animation/VFX reviews where AI handles administrative coordination while artists keep creative authority.

MOONWALK is a pre-production review system that articulates creative intent into a shared project record, anchors review judgments to grounded evidence, and converts authorized decisions into concrete revision tasks. AI components handle administrative coordination such as flagging missing context and organizing notes, not creative direction. An in-studio study against a chat-only AI interface showed stronger intent alignment, decision traceability, and checklist executability.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research

Authorization Architectures for Tool-Using AI Agents

Review paper proposes an authorization reference architecture for tool-using AI agents, identifying runtime enforcement and delegation bounds as unresolved gaps.

This review examines authorization models for tool-using AI agents that invoke APIs, databases, browsers, and protocols like MCP, arguing every consequential agent action must be traceable to a human principal, bounded by delegation, and contestable. It introduces a principal hierarchy spanning human user, operator/deployer, orchestrator agent, sub-agent, and tool endpoint, and analyzes five layers including credential lifecycle, delegation propagation, runtime enforcement, prompt injection as authorization bypass, and auditability. Drawing on 89 primary sources from 2023-2026, it proposes seven structural requirements, a four-layer reference architecture, and three deployable configurations.

arXiv cs.CR · 2d agoAI safety & security

AI Is Ending the Era of Hidden Vulnerabilities — Are Vendors Ready?

Dark Reading argues AI-assisted bug discovery is flooding vendors with vulnerability reports, straining disclosure processes and secure-by-design commitments.

The Dark Reading analysis describes a surge of bug reports driven by AI-powered discovery, exposing bottlenecks in vendor triage and disclosure pipelines. It argues this volume is revealing secure-by-design failures and questions whether vendors can keep pace with the rising tide of findings.

Dark Reading · 12d agoIndustry

Why 2026 is the Year to Upgrade to an Agentic AI SOC

Elastic Security Labs argues 2026 is the production inflection point for agentic AI in security operations centers.

Elastic Security Labs argues 2026 is the practical inflection point for agentic AI SOCs, noting nearly two-thirds of organizations are experimenting with AI agents while fewer than one in four have production deployments. The piece outlines operational challenges and recommendations: treat agents as non-human identities with least-privilege tool access, version-control system prompts as code, deploy unified agents with on-demand task packages, and enforce per-agent budgets and rate limits. It stresses explainability via RAG and transparent reasoning traces so analysts can verify and override autonomous decisions.

Elastic Security Labs · 8d agoIndustry

I'm going back to coding by hand

A developer abandoned six months of Claude-assisted work on a popular app, resuming hand coding after losing track of what AI-generated changes did.

In a Hacker News post, a developer of an app with a solid user base describes abandoning a six-month Claude-assisted rewrite because they no longer understood what the AI-generated code changed or broke. The author notes their workplace already writes code exclusively with LLMs and says returning to hand coding restored understanding and motivation. The post advises others feeling detached from their craft to stop using AI coding assistants for a few days.

Hacker News · security · 7d agoAI tools & infraHN 21↑ · 16 comments1

Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance

A study maps twenty inference-time AI governance mechanisms, finding commercial readiness only against cooperative deployers and no adequate defense versus state-level adversaries.

The paper develops a feasibility taxonomy of twenty inference-time AI governance mechanisms across monitoring, verification, and enforcement, each rated on a four-point readiness scale against a four-vendor evidence base. Fifteen of the twenty mechanisms have commercial technical substrates in production today, though governance-grade assurance and adversarial robustness vary substantially. Stress testing shows readiness holds only against a cooperative deployer and low-to-medium-capability user: no mechanism rates adequate against a high-capability state-level deployer, and fine-tuning removes model-internal enforcement components. A second-rater reliability check on readiness ratings returned a quadratic-weighted Cohen's kappa of 0.74.

arXiv cs.CR · 7d agoAI policy

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 · 29d agoResearch1