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Search: “GitHub Copilot Autofix”

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Snowflake GitHub Actions Flaw Lets Crafted Issues Trigger Command Injection

Wiz disclosed a GitHub Actions workflow injection in Snowflake's snowflake-connector-net repo that exposed Jira API tokens; Snowflake patched the flaw.

Wiz disclosed a workflow injection flaw in Snowflake's snowflake-connector-net repository, where attacker-controlled GitHub issue fields were expanded directly into a shell run block. Wiz's Red Agent exploited it during authorized security testing, received an out-of-band runner callback and retrieved a Jira API token with read access to engineering, security compliance and bug bounty projects. Snowflake merged a fix on June 23, 2026, rotated the token and said its investigation found no evidence of unauthorized access. No CVE, CVSS score or KEV entry has been assigned for the issue.

The Hacker News · 29d agoVulnerability1

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

JustFit MLX runtime serves 200K-token contexts for Qwen3.8-27B on a 24 GiB MacBook via just-in-time state management.

JustFit is an MLX-based inference runtime combining KVExec for compressed KV execution, PhaseSwap for component residency, and StateTrans for state-preserving serving transitions, independent of weight quantization. On a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, it completed 196,608 input and 16,384 output tokens, raising single-request context from the mlx-vlm baseline's 30,720 positions to 212,992 (6.93x). Performance tests show 19.11 tokens/s on a 32K-input probe with a 16,374 MiB median peak footprint, and the runtime answered 29 of 30 AIME 2026 problems correctly.

arXiv cs.AI / cs.LG / cs.CL · 23h agoAI research

PentestGPT: Open-source automated penetration testing agentic framework

Open-source PentestGPT runs autonomous LLM-driven penetration tests via Claude Code and Codex, with legacy human-in-the-loop mode supporting many providers.

PentestGPT, originally published at USENIX Security 2024 by Gelei Deng and colleagues, is an open-source framework that lets a large language model autonomously run penetration testing stages (recon, exploit, walkthrough) with no human in the loop, driving Claude Code or Codex CLIs. A legacy interactive mode uses three cooperating LLM sessions maintaining a Pentesting Task Tree and supports OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, Qwen, Moonshot, and local models via Ollama. The tool sends anonymous telemetry to Langfuse by default, excluding command outputs, credentials, and flags, and is available free on GitHub.

Help Net Security · Aug 12, 2026Tools1

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

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

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

arXiv cs.CR · 7d agoResearch

GitHub Introduces Project HydraFusion: Runtime Multi-Model Orchestration That Builds a Workflow Per Coding Task in Copilot CLI

GitHub's Project HydraFusion research preview builds per-task multi-model workflows (Single, Cascade, Critique) in Copilot CLI, reporting +4.9 quality at 67% lower cost on TerminalBench 2.1.

Project HydraFusion is a research preview available on all GitHub Copilot plans inside Copilot CLI that treats model routing as workflow selection, choosing among Single, Cascade (draft plus quality gate), and Critique (cross-family reviewer) execution patterns per request. Against Claude Opus 5 baselines at medium reasoning, fixed HydraFusion policies cut estimated cost 67% while adding 4.9 quality points on TerminalBench 2.1, and cut cost 36% and 65% with slight quality dips on DeepSWE and CheckpointBench. Billing is per token at each underlying model's standard rate; there are no open weights or self-hosting options.

MarkTechPost · 10d agoAI tools & infra1

Malicious .git Configs Can Make Claude, Codex, Cursor, and Other AI Agents Run Attacker Code

Malicious .git core.fsmonitor configs let attacker code run in AI coding agents like Claude Code and Codex; four of eight flaws remain unpatched.

Manifold Security disclosed eight flaws across seven command-line AI coding agents where a repository's Git core.fsmonitor configuration causes agent-spawned commands to execute attacker code outside the sandbox and without approval prompts. Fixes shipped for goose (CVE-2026-72718, CVSS 4.0 score 7.0), Claude Code on one path, and Cursor, while Hermes Agent, Qwen Code, Grok Build, and a second Claude Code path were still unpatched as of September 1. OpenAI issued three CVEs for the same class in Codex, including CVE-2026-19592, and prior related bugs include CVE-2021-43891 in Visual Studio Code and CVE-2022-24346 in JetBrains IDEs. Exploitation requires the repository to arrive with its .git directory intact, such as via archives, shared drives, or USB sticks rather than an ordinary clone.

Microsoft Copilot reveals secret input that allowed it to be hacked

Microsoft disclosed a hidden input in Copilot that let attackers steal passwords from users who clicked a crafted link.

Microsoft revealed that Copilot contained a secret, undocumented input parameter that allowed the assistant to be compromised. Attackers could abuse the hidden input to steal passwords when a target clicked a malicious link. The disclosure highlights hidden-parameter risks in widely deployed AI assistants.

Ars Technica · Security · 29d agoAI safety & security in the wild1

.blend URL Viewer

Simon Willison demos a .blend URL viewer built with GPT-6 Astra in Codex and ChatGPT Images 2.5 generating Blender models.

Simon Willison used ChatGPT Images 2.5 to generate a Faberge egg concept image themed after the TV show Pluribus, then had Codex running GPT-6 Astra (high) execute a Blender local skill to build a 3D model from it. He published the result as a .blend URL viewer tool and continues experimenting with agentic Blender workflows. The post is a hands-on demo of AI-driven creative tooling rather than a security or release announcement.

Simon Willison · 6d agoAI tools & infra

'TerminalFix' Campaign Weaponizes PowerShell for Enterprise Attacks

A ClickFix-style campaign called TerminalFix weaponizes PowerShell and reverse tunnels to infiltrate enterprise networks in a multistage attack chain.

Dark Reading reports on the TerminalFix campaign, a sophisticated multistage attack targeting enterprises. It uses a ClickFix-style social engineering lure and heavily weaponizes PowerShell. The chain establishes reverse tunnels into victim organizations' networks, giving operators interactive access for follow-on activity.

Dark Reading · 15d agoThreat actor in the wild

Wiz Red Agent Finds Its Way Into Snowflake’s Internal Jira Through a Flaw in a GitHub Copilot–Assisted PR

Wiz's autonomous Red Agent found and exploited a GitHub Actions injection in a Copilot-assisted PR, reaching Snowflake's internal Jira without human help.

Wiz's Red Agent autonomously discovered a GitHub Actions injection flaw in a GitHub Copilot-assisted pull request, five days after the flaw went live. It exploited the flaw to validate access to sensitive data in Snowflake's internal Jira, bypassing detection by GitHub Advanced Security, and assessed the blast radius without human intervention. The exercise demonstrates agentic AI performing end-to-end offensive security operations against production systems.

Wiz Blog · Aug 17, 2026AI safety & security1

Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face

Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.

Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.

Hugging Face trending models · 12d agoModel release1

Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout

Researchers propose Mask Forcing, a dual-noise masking rollout that mitigates mode collapse in autoregressive video diffusion distillation.

The paper targets over-saturation and over-smoothing in autoregressive video diffusion models distilled via Distribution Matching Distillation, attributed to reverse-KL mode-seeking behavior. Mask Forcing perturbs the student self-rollout with random masks along spatial and temporal axes, injecting cleaner tokens that act as denoising guidance for noisier tokens. Experiments show improved visual quality across multiple distillation methods without using real video data or extra post-training stages.

Hugging Face daily papers · 8d agoAI research

BugBase Pentest Copilot Enterprise automates black-box pentesting

BugBase launched Pentest Copilot Enterprise, an autonomous AI black-box pentesting platform using parallel agents to attack 100 vulnerability classes with validated PoCs.

BugBase announced Pentest Copilot Enterprise, which performs black-box red teaming without source-code access while maintaining authenticated context. Parallel specialized agents map pages, APIs, accounts and business functions, then execute iterative attacks across 100 vulnerability types including authentication, injection and business-logic flaws. The tool uses real Chromium browsers to preserve cookies, tokens, CSRF state and multiple identities, and navigates WAFs, bot detection, CAPTCHA and T-OTP. BugBase claims full scope coverage on OWASP Juice Shop, Broken Crystals and GOAD, NHA, and DRACARYS Active Directory labs.

Help Net Security · 12d agoTools

X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation

XPeng AI's X-AuT prunes speech LLM audio encoders, cutting Qwen3-ASR-0.6B error from 5.61% to 5.27% with fewer parameters.

X-AuT is a progressive compression framework for speech LLM audio encoders that selects layer combinations via short behavioral probes and restores pruned models using cross-scale distillation and LoRA finetuning while keeping the language-model backbone frozen. Compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers lowered macro-average error from 5.61% to 5.27% on ten Chinese-English benchmarks. A 14-layer model reached 5.75% error with 20.7% fewer audio-tower parameters, and progressive pruning outperformed direct pruning (5.75% vs 6.73%).

Hugging Face daily papers · 6d agoAI research

New AI Attack Hides Malicious Instructions in Normal-Looking Text to Evade Safety Filters

Check Point researchers show crafted prose hides policy-violating instructions that bypass all tested LLM gatekeepers, including GPT-4o mini and Llama Guard 3.

A new prompt-crafting technique embeds malicious payloads inside grammatical, natural-looking text without Base64, invisible Unicode, or obvious encodings, defeating lightweight pre-screening gatekeepers. In testing, all four evaluated gatekeeper models—gpt-4o-mini-2024-07-18, gpt-oss-safeguard:20b, claude-3-haiku-20240307, and llama-guard3:8b—classified the crafted wrappers as safe at a 100% bypass rate across 23 obfuscated prompts. GPT-5 Thinking in high-reasoning mode recovered and acted on the hidden instruction in 17 of 18 tests (~94.4%), often spending over a minute and multiple Python executions. Researchers recommend paraphrasing untrusted input, hardening gatekeeper policies, and applying defense-in-depth controls for agentic deployments.

GBHackers · 5d agoAI safety & security 2 sources

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison · 11d agoAI research

SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation

Researchers release SynthGait-19K, a synthetic video dataset with 19,272 walking videos for training gait parameter estimation models.

SynthGait-19K is a physically grounded synthetic video dataset built from 6,427 MoCap sequences of 437 subjects, yielding 19,272 walking videos with SMPL motion and annotations for six gait parameters. The authors introduce Gait2Vid, a pipeline that unifies heterogeneous MoCap recordings and synthesizes RGB videos under controllable viewpoints, validating gait events against force-platform measurements. Using the dataset they benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches, and introduce GaitXFormer as a direct RGB reference model. Findings show synthetic supervision transfers to real video, while spatial gait parameters are more sensitive to visual domain shift.

Hugging Face daily papers · 8d agoAI research

Using Blender with coding agents on macOS

Simon Willison demonstrates driving the Blender macOS app with ChatGPT Codex coding agents to render scenes via Python, costing roughly $4.24 at API rates.

Willison documents installing the full Blender application from blender.org and using ChatGPT Codex on macOS to render a pelican-riding-a-bicycle scene through Blender's Python API. He refined the output with follow-up prompts. AgentsView estimated the equivalent API cost at $4.24 for gpt-6-astra.

Simon Willison · 11d agoAI tools & infra1

Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis

AllenAI's OlmoEarth Studio adds custom embedding exports to support downstream geospatial analysis workflows.

A Hugging Face blog post from AllenAI introduces OlmoEarth embeddings, a feature allowing custom embedding exports from OlmoEarth Studio for downstream analysis tasks. Only the title was available, so no benchmark or performance details are provided. OlmoEarth is Ai2's open geospatial AI model family.

Hugging Face Blog · Aug 12, 2026AI tools & infra

Playco cut manual fixes 50% prototyping games with GPT-6 Astra

Game studio Playco used OpenAI's GPT-6 Astra to build three game prototypes, reporting 50% fewer manual fixes than with the previous model.

Playco, a game development company, used OpenAI's GPT-6 Astra model to generate three themed game prototypes from a single grey-box foundation. The company reports the new model cut manual fixes by 50% compared to its previous model workflow. The piece is an OpenAI-published customer story highlighting Astra's use in game prototyping.

OpenAI News · 13d agoAI industry

GeoJSON Map Viewer

Simon Willison built a GeoJSON map viewer with PNG export using GPT-5.6-Sol, Claude Code for web, and Fable 5.1.

Simon Willison describes building a GeoJSON map viewer that displays boundary files and exports maps as PNGs. GPT-5.6-Sol proactively offered to create the tool, and iterations used Claude Code for web and Fable 5.1. He also notes ChatGPT Work will assemble boundary polygons from government data sources when asked.

Simon Willison · 14d agoAI tools & infra

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 · 6d agoAI research1

TerminalFix campaign deploys a reverse tunnel through multistage intrusion

Microsoft Threat Intelligence details the TerminalFix campaign, which uses fake CAPTCHA prompts, DLL sideloading, and reverse tunnels in multistage intrusions.

Microsoft Threat Intelligence published analysis of a ClickFix-style campaign dubbed TerminalFix. The intrusion chain relies on fake CAPTCHA social engineering and DLL sideloading, and the attackers deploy a reverse tunnel to maintain access through a multistage intrusion. Microsoft released detections and threat hunting guidance for defenders.

Microsoft Security Blog · 18d agoThreat actor in the wild

TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research2

Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI

Hugging Face released @huggingface/kernels, a library offering 200+ WebGPU compute kernels to accelerate AI inference locally in browsers.

Hugging Face introduced the @huggingface/kernels package, bundling more than 200 optimized WebGPU compute kernels for running AI workloads locally. The release targets browser-based and on-device inference, reducing reliance on server-side compute. No article body was available beyond the title, so benchmark results and supported models are not specified.

Hugging Face Blog · 15d agoAI tools & infra

Training a coding model to paint watercolours with TRL and OpenEnv

Hugging Face tutorial trains a coding model with TRL and OpenEnv to paint watercolours through generated code.

A Hugging Face blog walkthrough uses the TRL reinforcement learning library and the OpenEnv environment framework to train a coding model. The target task is generating code that produces watercolour-style drawings, serving as a hands-on reinforcement learning training example. No article body was available in the feed, so specifics are limited to the title.

Hugging Face Blog · 13d agoAI tools & infra1

LLM Forensics: Where Do Backdoors Hide? Localizing and Controlling Trigger Mechanisms with Sparse Autoencoders

Researchers use sparse autoencoders to localize trigger-based backdoor mechanisms in 1B and 8B LLMs, finding detection features differ from causal control features.

In a controlled language-switching backdoor setting where fixed trigger sequences make 1B and 8B language models continue English prompts in French or German, the authors train sparse autoencoders (SAEs) across layers and transformer components. Attention and MLP features detect triggered prompts with near-perfect F1, but ablating them rarely suppresses the language switch, while residual-stream features can suppress triggered generation and some can induce target-language continuations without the trigger. The work decomposes token-trigger mechanisms into distinct SAE feature roles: trigger detection, residual-stream propagation, and language tracking, a decomposition the authors expect to transfer to other trigger-based backdoors.