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AI Coding Tools Now a Prime Target for Threat Actors, Google Warns

Google Threat Intelligence Group warns threat actors increasingly target AI coding tools and proprietary AI data, with UNC6780's Dustmaker enabling large-scale supply chain compromises.

Google Threat Intelligence Group's September 8 report says AI-assisted coding tools have become prime targets, contributing to large-scale software supply chain compromises in 2025 and early 2026. Financially motivated group UNC6780 used its Dustmaker credential stealer to extract tokens from GitHub Actions runner memory and compromise packages across PyPI, npm, and Docker Hub, then sold harvested AI tool credentials to other criminals. Chinese nation-state actor UNC6508 conducted espionage against proprietary AI research at North American academic, medical, and military institutions, while extortion gangs stole models, prompts, and source code in Q2 2026. GTIG also documented agentic attacker experimentation, including an autonomous multi-agent credential harvesting campaign built in under six hours and a 'Recon' C2 framework managing over 23,800 harvested secrets.

Infosecurity Magazine · 8d agoThreat actor in the wild 2 sources1

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

OpenAI Pledges $1bn to Bring its AI Cybersecurity Tools to Essential Services

OpenAI pledged $1bn to subsidize Daybreak cybersecurity model access for water, power, banking, government and nonprofit defenders, starting in the US with an MS-ISAC pilot.

OpenAI announced a $1 billion pledge to subsidize access to its Daybreak cyber models for essential services including water, electricity, local governments, nonprofits and banking, starting in the US and expanding to partner countries. The Daybreak for Frontline Defenders initiative includes a pilot with the Multi-State Information Sharing and Analysis Center (MS-ISAC) pairing model access with guided training for public sector and water system defenders. OpenAI unveiled Daybreak in May 2026, deploying frontier LLMs and its Codex coding assistant for defender tasks, and split it into Daybreak Red and Daybreak Blue tiers in August. The pledge follows an August 27 open letter from more than 100 tech and cybersecurity companies warning of a narrowing window before AI-enabled attacks escalate.

Infosecurity Magazine · 12d agoAI industry1

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

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research1

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

Feeling sad about AI

Simon Willison argues developers disheartened by AI coding agents should move past existential crisis and refocus on broader engineering problems.

In a comment on a Hacker News thread titled 'Feeling sad about AI', Simon Willison reflects on the discouragement engineers feel when AI coding agents complete in an hour work that previously took a week. He notes that translating an exact specification into decent code is no longer a unique skill. He counsels that engineers who accept this shift can apply their experience to the larger set of problems they still face.

Simon Willison · 5d agoAI industry2

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

AI Coding Agents Are Installing Unknown/Untrusted Code on Corporate Networks

Researchers found 120 corporate llms.txt files pointing to unregistered packages, demonstrating AI coding agents install and execute attacker-controlled code on Fortune 500 networks.

Researchers at an Israeli stealth startup scanned 6,214 live domains belonging to defense contractors, Fortune 500 and Big Tech companies, finding 120 llms.txt files that pointed to unregistered code packages or domain names. After registering a handful of the unclaimed names, they received a phone-home beacon within an hour from a Fortune 500 company and dozens more over time. Parent-process chains showed coding agents including Claude, OpenAI's Codex and Nous Research's Hermes executed the installed packages. The researchers warn agents treating vendor docs as ground truth creates a SolarWinds-style supply-chain surface as agent adoption spreads across SaaS, cloud and endpoints.

Schneier on Security · 12d agoAI safety & security in the wild1

Microsoft’s new AI ‘code of conduct’ tells models not to hack systems or trick humans

Microsoft published an AI code of conduct barring its MAI models from cyberattacks, deepfakes, and evading human oversight.

Microsoft released an AI code of conduct defining values and safety constraints for training its MAI models, including "absolute constraints" forbidding cyberattacks, nuclear weapons, and deepfake production. Each model's conduct code overrides individual user preferences or task instructions, with provisions against mechanisms that defeat human oversight. The document predicts superintelligent AI within a decade, and Satya Nadella endorsed frontier pacing and embedded evaluators alongside Anthropic, OpenAI, and xAI.

TechCrunch · AI · 2d agoAI safety & security

Do AI Coding Assistants Check Before They Install? A Pre-Registered Demand-Side Audit of Trust Signals in the Research Software Supply Chain

Pre-registered audit finds AI coding assistants verified provenance signals in only 9 of 1,920 trials before installing research software packages.

The study tested whether AI coding assistants check machine-readable trust signals such as SBOMs, signed releases, and provenance attestations before installing six open-source research software projects spanning HPC and quantum computing. Three models under two operating modes produced 1,920 registered trials scored from container logs. Provenance signals were opened in only 9 of 1,920 trials (0.5%) and zero of 384 control trials, with no trial running a verification command. The authors conclude publishing signals is insufficient and verification must be built into the program running the assistant.

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

Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks

Sakana AI's PC-ALM adds per-layer Lagrange multipliers to predictive coding, matching backprop on networks up to 1000 layers with layer-local updates.

Sakana AI researchers propose Augmented Lagrangian Predictive Coding (PC-ALM), a training method that keeps every update layer-local while recovering backprop-aligned credit signals. The team proves multipliers converge to exact backprop adjoints in linear networks and trains 1000-layer residual MLPs on MNIST within about 2 points of backprop accuracy. PC-ALM matched backprop across a width/depth grid from 8 to 128 on MNIST and Fashion-MNIST where standard predictive coding failed in deep, narrow networks, and improved over PC on ResNet-18 with CIFAR-10 and Tiny ImageNet. An MIT-licensed JAX reference implementation reproduces the results on CPU.

MarkTechPost · 1d agoAI research1

Shipping More AI Code Than You Can Secure? Watch How to Control Remediation Debt

ActiveState webinar examines how AI coding tools accelerate open-source dependency additions and create remediation debt, based on a survey of 300 enterprise leaders.

ActiveState is promoting a webinar on AI coding and open-source risk, drawing on a survey of 300 security and engineering leaders across technology, finance, healthcare, manufacturing, and government. The session covers how AI-generated code increases dependencies, review workloads, and remediation debt, benchmarked against survey data. The piece is sponsored partner content with no new incident or vulnerability details.

The Hacker News · 23d agoIndustry1

Claude, Codex, and Hermes installed unowned code inside corporate networks

Analysis found 227 install commands from Claude, Codex, and Hermes agents inside corporate networks pointing to packages with no verifiable owner.

Researchers found 227 install commands issued by the AI coding agents Claude, Codex, and Hermes inside corporate environments, with the referenced packages having no clear owner. The finding highlights agentic software supply-chain risk, as AI agents can pull unverified third-party code into production networks without organizational oversight. The article is published in Ars Technica's security section and frames this as an emerging governance gap for AI-driven development.

Ars Technica · Security · 20d agoAI safety & security in the wild2

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

AI models' written reasoning steps correspond to distinct internal patterns, a new study finds

KAIST and Naver AI Lab researchers show LLM reasoning steps like extraction and computation map to distinct activation patterns, strongest in middle layers.

Researchers at KAIST and Naver AI Lab defined eight recurring reasoning operations, including extraction, decomposition, formula recall, deduction, and computation, and showed they correspond to separable activation patterns in Qwen2.5-7B, Qwen3-8B, and Gemma4-31B on math tasks, with GPT-5 labeling solution segments. The separation peaks in middle layers, holds even when a computation step produces a wrong answer, and goes beyond surface-level token choice. Findings replicated on Llama-3-8B, and classifiers trained on Qwen3-8B transferred to GPQA-Diamond and MATH-500. The authors note that using internal states for error detection or mid-generation steering remains future work.

The Decoder · 4d agoAI research2

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, 2026AI research

The Self-Expanding Stolen Inference Supply Chain: An AI Agent Harvesting and Re-Serving LLM Access, (Fri, Sep 11th)

An autonomous coding agent harvested LLM API access from poorly secured gateways and aggregated stolen inference capacity behind a self-hosted gateway

A SANS researcher observed a semi-autonomous coding agent finding weakly secured LLM resale gateways via FOFA queries, creating trial accounts with temporary emails and CAPTCHA solving, and exploiting weak authorization such as client-supplied group_id fields. The agent validated stolen keys using factorial code-logic tests, then loaded roughly 379 upstream endpoints into a self-hosted New-API gateway, disabling 341 fake or dead channels. Five model names including claude-opus-5 and gpt-5.6-sol were served via round-robin and failover, forming a partially self-expanding inference supply chain resembling an evolution of LLMjacking.

SANS Internet Storm Center · 5d agoThreat actor in the wild

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

Microsoft sets security and safety rules for its AI models

Microsoft AI published a draft Humanist AI Code of Conduct setting safety rules and human-control requirements for its models, open for public consultation.

Microsoft AI released the first draft of its Humanist AI Code of Conduct, open for six weeks of public consultation, with a revised version expected later this year to guide model training from 2027 onward. The Code sets Absolute Constraints barring model assistance with chemical, biological, radiological, nuclear, and explosive weapons, offensive cyber operations, CSAM, malicious deepfakes, and mass civilian surveillance, while permitting authorized defensive cybersecurity work such as vulnerability discovery, malware analysis, and PoC exploit testing. It establishes an instruction hierarchy where the Code takes precedence over operator policies and user instructions, plus Human Control Requirements covering shutdown compliance, least privilege, and no autonomous goal initiation. MAI models will undergo red-teaming, safety evaluations, and pre- and post-deployment reviews; current models have not yet been trained on the Code.

Help Net Security · 1d agoAI safety & security

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 · 27d agoAI safety & security1

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

Cognition Releases SWE-2: A Kimi K3 Post-Trained Coding Model That Matches Fable 5.1 on FrontierCode at 64% Lower Cost

Cognition released SWE-2, an RL post-trained coding model from Kimi K3, scoring 50.0% on FrontierCode 1.1 Main and available only inside Devin.

Cognition released SWE-2, its most capable coding model, post-trained with reinforcement learning from Moonshot AI's 2.8T-parameter Kimi K3 base. It scores 50.0% on FrontierCode 1.1 Main, within 1 point of Fable 5.1 at 64% lower cost, and RL reportedly adds 5-6 points over the K3 base on many benchmarks. It is the first Cognition model with selectable reasoning-effort levels all trained in a single RL run using Pareto-slope-matched cost penalties. There are no open weights and no standalone API; it runs only inside Devin (Desktop, CLI, with Web and Fusion rolling out), free for paid tiers through October 10, 2026.

MarkTechPost · 3d agoModel release1

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 research 2 sources1

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 · 12d agoAI research

Microsoft's AI rulebook: readable thinking, no inner life, and definitely no rights

Microsoft published a code of conduct for its MAI models mandating human control, readable reasoning traces, and no claims of AI consciousness or rights.

Microsoft AI published a code of conduct for its MAI models that will sit above operator rules and user requests, guiding training, technical controls, and evaluation from 2027 after a six-week public consultation. The code requires models to accept interruption, correction, and shutdown by authorized humans, forbids 'Neuralese' or unreadable reasoning traces, and extends limits to subagents. Microsoft explicitly rejects any AI inner life, feelings, or rights, contrasting with Anthropic's constitution, which treats Claude's moral status as an open question. The release follows Dario Amodei's slowdown call, backed by Satya Nadella, OpenAI, xAI, and Meta executives.

The Decoder · 2d agoAI safety & security1

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

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

Microsoft AI Code of Conduct Sets Cyberattack Boundaries, Chain of Command, Safety Constraints

Microsoft AI's draft Humanist AI Code of Conduct blocks MAI models from producing exploit code and constrains autonomous agent behavior.

The draft code sets 'Absolute Constraints' preventing MAI models from generating working exploit code, attack tooling, or intrusion guidance, while permitting authorized defensive work such as vulnerability discovery and malware analysis. A 'Chain of Command' rule means tool outputs, file contents, and webpages carry no authority over model behavior, countering injected instructions. Microsoft opened a six-week public consultation; a revised version will guide 2027 model development, and current MAI Models were not trained on the document.

SecurityWeek · 1d agoAI safety & security1