Search: “code-org”

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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·Aug 27, 14:00 UTC · 19d agoAI safety & security in the wild2

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Show HN: Self-hosted company OS, Claude Code and Codex agents in departments

OtoDock, a self-hosted company OS that organizes Claude Code and Codex AI agents into departments, was launched on GitHub via Show HN.

OtoDock is a self-hosted 'company OS' shared on GitHub through a Show HN post, presenting Claude Code and Codex AI agents organized into department-style teams. The Hacker News feed entry shows the post reached 20 points with 5 comments; no further technical details are provided in the available text.

Hacker News · security·Sep 9, 17:57 UTC · 6d agoAI tools & infraHN 20↑ · 5 comments1

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Coop – Isolated VM Environments for Running Claude Code and Codex

Trail of Bits releases Coop, running Claude Code and OpenAI Codex agents inside isolated virtual machines for safer agentic coding.

Coop, published on GitHub by security firm Trail of Bits, provides isolated VM environments for executing AI coding agents such as Claude Code and Codex. Isolation contains the filesystem and network side effects of autonomous agent actions, reducing risk from unsupervised tool use. The project drew 61 points and 16 comments on Hacker News.

Hacker News · AI·Sep 7, 04:18 UTC · 9d agoAI tools & infraHN 61↑ · 16 comments1

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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·Sep 4, 10:35 UTC · 12d agoAI safety & security in the wild1

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Code to Cloud Platform Archives

Unit 42 archive page for Code to Cloud platform product-category articles; no new research or announcements.

This Unit 42 archive page lists product-category posts about Palo Alto Networks' Code to Cloud platform, which secures public cloud environments, SaaS applications and remote locations. It contains no new research, incidents, or announcements.

Palo Alto Unit 42·Aug 19, 12:04 UTC · 28d agoIndustry

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Ask HN: Anyone still coding like 2021? Where do you work?

Hacker News users debate coding without LLMs, with one developer fired for refusing AI tools and others describing daily hand-coding practice to counter skill atrophy.

An Ask HN thread collects experiences of developers who still write code without LLM assistance. One contributor says he was fired for political reasons after refusing to use LLMs despite adequate stated performance, and observes fewer job ads now require LLM use. Others describe starting each day with a LeetCode problem or 30-60 minutes of hand-coding to stay sharp, contractual bans on AI-generated code for a government-adjacent embedded product over unresolved copyright issues, and inconsistent corporate policies where ChatGPT or Codex use flip-flops between allowed and blocked while a CIO mandates 70-80% AI-generated code next year.

Hacker News · AI·Sep 9, 13:41 UTC · 6d agoAI industryHN 47↑ · 54 comments2

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[webapps] Langflow 1.8.4 - Path Traversal to Remote Code Execution

A path traversal to remote code execution exploit for Langflow 1.8.4, a popular LLM application builder, was published on Exploit-DB.

Exploit-DB lists a proof-of-concept exploit chaining path traversal to remote code execution in Langflow 1.8.4, an open-source tool used to build LLM applications and agents. The chain allows an attacker to write arbitrary files outside the intended directory and achieve code execution on the host. The provided text does not include a CVE identifier or reports of exploitation in the wild, but RCE in a widely deployed AI tooling product is notable for defenders.

Exploit-DB·Aug 31, 00:00 UTC · 16d agoExploit / PoC1

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Flextype v1.0.0-alpha.3 CMS registerShortcodes() Remote Code Execution via Attacker-Controlled File Inclusion

Flextype CMS v1.0.0-alpha.3 allows PHP remote code execution via path traversal in the Entries API combined with shortcode file inclusion.

Flextype CMS v1.0.0-alpha.3 exposes a remote code execution path through the interaction of the Entries API and Shortcodes::registerShortcodes(). The /api/v1/entries endpoint accepts attacker-controlled entry identifiers containing path traversal sequences, allowing PHP-containing content to be written outside the intended entries directory. A subsequent attacker-controlled path can then be included and executed as PHP. Ron E posted the disclosure to the Full Disclosure mailing list on September 3, 2026.

Full Disclosure·Sep 4, 00:13 UTC · 12d agoVulnerability 8 sources

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C*: Unifying Programming and Verification in C

Research paper C* (arXiv:2504.02246) proposes unifying programming and formal verification in C, discussed on Hacker News.

The arXiv paper 2504.02246, titled C*: Unifying Programming and Verification in C, presents an approach that combines C programming with built-in formal verification. The item surfaced via Hacker News, where it reached 28 points and 18 comments. Detailed abstract content was not included in the source text.

Hacker News · security·Sep 8, 15:56 UTC · 7d agoResearchHN 28↑ · 18 comments

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Show HN: Ordewell – turn one goal into an ordered plan of coding-agent tasks

Ordewell is an open-source tool that decomposes a single goal into an ordered plan of coding-agent tasks, posted on Hacker News.

Ordewell, shared as a Show HN project on GitHub, converts one high-level goal into an ordered plan of tasks for coding agents to execute. The post received 40 points and 29 comments on Hacker News. It focuses on task planning and orchestration for autonomous coding agents.

Hacker News · AI·Sep 15, 13:31 UTC · 1d agoAI tools & infraHN 40↑ · 29 comments

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Putting models to the secure coding test: Plan vs default mode

Datadog Security Labs tested Sonnet 5, Composer 2.5, and GPT 5.5 to see if plan mode yields more secure code than default mode.

Datadog Security Labs evaluated whether plan mode produces measurably more secure code than default mode. The test covered three frontier coding models: Sonnet 5, Composer 2.5, and GPT 5.5. The results inform how engineering teams should configure AI coding assistants to reduce insecure code. This is an AI security evaluation, not an incident report.

Datadog Security Labs·Aug 19, 00:00 UTC · 28d agoAI safety & security

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Senior engineers are spending their week cleaning up AI-generated code

New Relic study finds AI-generated code doubles critical runtime issues, with senior engineers losing a third of their week to fixes.

A New Relic survey of U.S. technology leaders reports AI now writes the majority of shipped code, with senior SRE and DevOps engineers spending up to a third of their week triaging and refactoring it. A large majority of organizations had at least one AI-related production failure in the past six months, and roughly three in ten saw newly introduced security vulnerabilities. AI-generated code showed nearly twice as many critical runtime issues as peer-reviewed human-authored code, with gaps concentrated in edge cases, concurrency, deprecated APIs, and complex state changes. Most teams now prompt AI tools to embed logs and traces directly into generated code.

Help Net Security·Aug 24, 04:22 UTC · 23d agoAI industry1

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

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

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

MarkTechPost·Sep 9, 22:58 UTC · 6d agoAI tools & infra

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Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead

Audit of 254 SWE-bench submissions finds top coding-agent entries statistically inseparable, so small leaderboard gaps no longer establish rank.

The paper audits 254 SWE-bench submissions across four splits without running models. On Verified, the top two entries each resolve 396 of 500 instances, and exact paired McNemar tests separate none of the 29 adjacent top-thirty pairs at alpha=0.05. Within-model scaffold score ranges reach 29.8 percentage points, versus an 8.8-point spread among the top thirty. The authors release a five-step audit protocol and recommend reporting comparison-set-specific resolution and model-scaffold provenance.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:29 UTC · 21h agoAI research

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An Empirical Analysis of CodeQL False Positives and Query Refinements for Java Vulnerabilities

Study of 167 Java CVE instances finds CodeQL false positives follow recurring patterns; query refinements remove 81.8% of reviewed ones.

Researchers ran CodeQL's Java security query suite on 167 CVE instances from 110 projects, manually reviewing 500 sampled false-positive paths and building a five-category taxonomy led by Missed Path Constraint or Sanitization (36.6%), Benign Execution Context (29.4%), and Missing Trust Boundary Modeling (27.6%). Guided by the taxonomy, query-level refinements removed 81.8% of reviewed false positives and 15.8% of reported paths across the selected queries while retaining 7 of 8 true positives. To address generalization, agentic coding tools given the refinement patterns as templates adapted them to new projects successfully in 56% and 62% of tasks, versus 28% without guidance.

arXiv cs.CR·Sep 3, 22:48 UTC · 12d agoResearch1

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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·Sep 15, 09:40 UTC · 1d agoAI safety & security1

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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

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How well do agents use test/verification techniques?

Dan Luu's eval finds coding-agent testing instructions (TDD, formal methods, PBT, skills) mostly fail to beat defaults on Zstd implementation correctness.

The author ran 26 prompt conditions plus 4 skills on a Zstd-in-Rust implementation eval using codex with GPT-5.6, testing TDD, fuzzing, property-based testing, formal methods (Lean 4, TLA+, Verus, Kani, SMT solvers) and community skills. Nothing dramatically outperformed the default no-instruction condition, which did above average; at xhigh effort, fuzzing and PBT conditions did slightly better than formal methods. Pre-registered predictions included TDD underperforming and popular test skills (ECC, Hegel, Trail of Bits) not outperforming. Results are averages of 80 runs per condition plotted against cost.

Hacker News · security·Sep 8, 02:58 UTC · 8d agoAI researchHN 131↑ · 50 comments

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[webapps] Langflow 1.10.0 - RCE

A public proof-of-concept demonstrates remote code execution in Langflow 1.10.0, the open-source AI workflow builder.

Exploit-DB lists a remote code execution proof of concept targeting Langflow version 1.10.0. Langflow is a widely used open-source tool for building AI agent and LLM workflows. The listing does not include a CVE id or evidence of in-the-wild exploitation, but RCE in AI development tooling is relevant to teams running it in production.

Exploit-DB·Sep 2, 00:00 UTC · 14d agoExploit / PoC1

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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

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Retrofitting Code Using LLMs to Support Exceptional Behavior

EXCODER combines static/dynamic analysis with LLMs to retrofit exception-handling code, achieving 85.92% pass@1 with Qwen 2.5 Coder 32B on Java benchmarks.

The paper introduces the task of retrofitting existing code with Exception Related Code (throw statements, guarding conditions, try/catch blocks) so that given Exceptional Behavior Tests pass. EXCODER performs context engineering by integrating static and dynamic program analysis output with LLMs; it was evaluated on a benchmark built from 304 methods across 75 GitHub Java projects. Combined with Qwen 2.5 Coder 32B, EXCODER achieves pass@1, 5, and 10 rates of 85.92%, 86.18%, and 86.51%, roughly 13 percentage points over baseline, and manual inspection reveals remaining limitations.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:18 UTC · 6d agoAI research1

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Compiler Can Undo Your Security Checks

Chris Domas explains at Black Hat how legal compiler optimizations can strip security checks and memory-clearing operations, turning secure C source into vulnerable binaries.

David Bombal interviews researcher Chris Domas at Black Hat USA 2026 about how the C abstract machine permits compilers to legally transform code in ways that remove security protections, delete memory-clearing operations, and introduce time-of-check to time-of-use vulnerabilities. Factors like register pressure, structure layout, and data size affect vulnerability, with examples where 17 or 33 byte buffers are safe while nearby sizes produce vulnerable code. An AI-assisted analysis of 500 million lines of open-source code identified 300 potentially dangerous patterns. Recommended mitigations include enabling compiler warnings, using sanitizers, analyzing optimized builds, and testing the exact binary that ships.

Hacker News · security·Sep 12, 14:07 UTC · 3d agoResearchHN 22↑ · 27 comments

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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·Sep 14, 16:27 UTC · 1d agoAI safety & security

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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·Sep 7, 17:26 UTC · 8d agoAI research1

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Can your coding style predict whether your code is vulnerable?

University of Massachusetts Dartmouth researchers present VulStyle, a stylometry-based vulnerability detector that also exposes benchmark reliability problems.

VulStyle combines stylometric features with syntax-tree structure and source tokens, pre-trained on about 4.9 million functions across seven programming languages and fine-tuned on five vulnerability detection datasets. It beat token-only detectors on some benchmarks but its F1 drops sharply on DiverseVul, which the authors link to noisy labels inflating reported performance across popular datasets. The authors argue style-aware detection should be harder to evade but did not test this empirically, and they note that uniform LLM-generated code may strip away the individual developer style the model depends on.

Help Net Security·Aug 24, 04:21 UTC · 23d agoResearch1

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SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code

SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.

SEMA-GUARD is a framework that detects vulnerabilities in compiled programs when source code is unavailable, targeting malware, firmware, and embedded systems analysis. It enriches control flow graphs with low-level execution semantics including stack manipulations, memory accesses, and data flow. Evaluated on a Juliet Test Suite set compiled to assembly and split into function-level chunks, it achieves 85.1% accuracy and an F1 score of 0.801, outperforming purely statistical or structural approaches.

arXiv cs.CR·Sep 15, 14:35 UTC · 23h agoResearch1

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