Search: “ghostcode”

GhostCode Abuses Microsoft Entra Device Enrollment to Maintain Access After Token Revocation

eSentire exposes GhostCode, a device-code phishing kit that abuses Microsoft Entra device enrollment to persist even after stolen tokens are revoked.

eSentire's Threat Response Unit observed GhostCode campaigns in late August 2026, using BEC-style social engineering that impersonated procurement staff, including BJ's Wholesale Club, via Salesforce contact forms. Victims received password-protected HTML lures disguised as a FlipBook document portal, with junk-data padding, HTML comment injection, and AES-256-GCM encrypted redirects gated by anti-bot checks. The kit exploits the OAuth 2.0 device authorization grant, prompting victims to approve real Microsoft device-code sign-ins with MFA. Within 78 seconds of approval, attackers registered three Entra devices and obtained a Primary Refresh Token, so rogue device registrations persist even after session token revocation.

GBHackers·Sep 16, 13:41 UTC · 12h agoPhishing & fraud in the wild2

Domain-Specific Hallucination Detection in Large Language Models

A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.

The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:45 UTC · 6d agoAI research1

GhostCode Phishing Kit Bypasses Microsoft 365 MFA to Hijack Accounts in 78 Seconds

eSentire identified GhostCode, a phishing kit abusing Microsoft 365 OAuth device-code sign-in to steal tokens and take over accounts in seconds.

eSentire analysts identified GhostCode in late August, a phishing kit that uses business contact-form messages and an NDA pretext to deliver a password-protected HTML attachment leading victims to a Microsoft device-code sign-in. Victims authenticate on legitimate Microsoft pages, letting the kit obtain a Primary Refresh Token in 32 seconds and register three devices in 78 seconds, with residential proxies matching the victim's location. The kit hides its redirect with encrypted addresses, junk data, and scanner-filtering challenges, and uses GHOSTnet-linked infrastructure during device enrolment. eSentire recommends blocking device-code authentication via Conditional Access, invalidating tokens, and reviewing newly enrolled devices.

Cyber Security News·Sep 16, 14:54 UTC · 11h agoPhishing & fraud in the wild 9 sources5

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 · 9d agoAI research1

The VMs Powering Mobile Agents (Instinct, Claude Code)

A teardown reveals Claude Code runs in Firecracker microVMs with a Rust PID 1 and MITM'd egress, while Instinct rents E2B sandboxes with git-based memory.

The author inspects the virtual machines hosting cloud agents: Claude Code runs in a Firecracker microVM with a custom Rust init (process_api) as PID 1, a 324 MB Bun harness on a read-only disk, and 443-only MITM'd SSE egress to api.anthropic.com with host-rotated OAuth tokens and no inbound access. Instinct rents E2B sandbox-as-a-service Firecracker microVMs (Ubuntu 22.04, 2 vCPU, 1.9 GB RAM) where agent memory is a git repo of Markdown committed by the agent and pushed to S3 as a single bundle, using short-lived STS credentials. Both platforms rely on Firecracker, differing mainly in fleet operator and guest boot configuration.

Hacker News · AI·Sep 8, 04:36 UTC · 8d agoAI tools & infraHN 58↑ · 19 comments1

How Lossless Is Lossless Speculative Decoding? The Role of Numerical Precision in Orthrus

Reproduction study finds Orthrus speculative-decoding trajectories match the reference model in only ~45% of cases under BF16, but 100% under FP32.

Researchers independently reproduced Orthrus, a hybrid autoregressive-diffusion architecture claiming lossless speculative decoding via intra-model consensus, testing exact trajectory matching on 1,190 prompts across 12 domains. Under BF16, exact matching occurred in only 45% of cases for the authors' checkpoint and 43% for an independently trained model, with matching probability strongly tied to reference-model response-conditional perplexity. Despite trajectory divergence, downstream lm-eval-harness benchmarks showed no systematic degradation, while FP32 evaluation yielded exact matching on all prompts.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI research1

The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center·Aug 31, 20:00 UTC · 16d agoAI safety & security1

“Ghostjacking” Exploits AI Agents’ Trusted Access to Evade Firewall Controls

Tenet warns 'Ghostjacking' tricks AI agents with fake reports to abuse trusted access and bypass firewall controls, exposing half of Fortune 500 firms.

Tenet researchers described 'Ghostjacking,' a technique that feeds fabricated reports to AI agents in order to hijack their trusted access and evade firewall controls. The firm estimates that roughly half of Fortune 500 companies are vulnerable because AI agents operate with elevated, trusted permissions that perimeter tools do not inspect.

Infosecurity Magazine·Aug 10, 10:45 UTC · Aug 10, 2026AI safety & security

Post-DEF CON Phishing Uses Malicious Google Doc to Deliver Malware

Huntress uncovered post-DEF CON phishing via X direct messages using a malicious Google Doc to deliver AMOS and NetSupport RAT malware.

Huntress uncovered a phishing campaign targeting attendees after Black Hat and DEF CON. Attackers used X direct messages pointing to a malicious Google Doc as the delivery vehicle. Payloads include AMOS, a macOS infostealer, and the NetSupport RAT, among other malware.

Huntress·Aug 19, 13:00 UTC · 28d agoPhishing & fraud

My business partner sent a 5K vibe-coded PR that he didn't even test

A developer's business partner shipped a 5,236-line untested vibe-coded payments backend PR whose endpoints failed basic testing.

The author describes reviewing a pull request with 5,236 additions for a payments backend that a business partner generated largely with AI in a single day without testing. The PR's AI-written documentation included redundant boilerplate (e.g., 'returns 400 on error') but omitted operational details like where to obtain API keys, and the endpoints failed when tested. The post is a critical opinion piece on vibe coding and perceived skill atrophy among developers who rely on AI for everything.

Hacker News · security·Sep 8, 01:48 UTC · 9d agoAI industryHN 46↑ · 75 comments1

Enoki: Efficient Multi-Level Hallucination Detection

Researchers introduce Enoki, an open information extraction framework unifying claim-level and span-level hallucination detection in LLMs at lower inference cost.

Enoki extracts text-anchored relational facts, verifies them against evidence, and projects unsupported facts back onto hallucinated spans, so claim-level verification and span-level localization share one representation without separate alignment. It supports LLM-based, encoder-based, and rule-based extraction regimes to balance accuracy against inference cost. Experiments show it stays competitive with strong claim-level systems while using fewer resources and outperforms them on fine-grained span- and entity-level localization. The authors also release EnokiQA, a dual-granularity dataset with aligned claim-level verification and span-level localization annotations.

Hugging Face daily papers·Aug 31, 22:21 UTC · 16d agoAI research

ThreatsDay: GhostJacking AI Attacks, EtherHiding ClickFix, Cursor CLI Flaw + 17 More Stories

A weekly bulletin aggregating short security updates, including the City-Forum data-theft campaign, a ShipMonk breach, a Cursor CLI flaw, and GhostJacking AI attacks.

The Hacker News ThreatsDay Bulletin bundles roughly 20 short updates across cloud services, AI tools, malware, breaches, and scams. Highlights include the City-Forum campaign pulling data from unauthenticated guest access in Salesforce Experience Cloud and ServiceNow Service Portals since March 2025, and a ShipMonk breach exposing Trezor customer order data for orders in seven countries between May 10 and August 8, 2026. Other items cover a patched Cursor CLI flaw that let cloned repositories run commands before the workspace-trust prompt, Okta's analysis of the Work Panel vishing console used by actors like UNC6671, and GhostJacking AI agent hijacking via a patched Claude Desktop sandbox escape. Meta also launched an on-device WhatsApp Scam Alert machine learning model that keeps message content on the device.

The Hacker News·Aug 18, 16:30 UTC · 29d agoIndustry

$536 and 8 Hours: AI Learns to Attack a Different PLC

Forescout used Claude to port a WAGO PLC exploit for $536 in 8 hours, and an AI-generated payload later permanently bricked the test device.

Forescout researchers used Claude Code with Ghidra, terminal access, and physical hardware to port CVE-2021-31886, a pre-authentication buffer overflow in the Nucleus FTP server, from a WAGO 750-852 PLC to the WAGO 750-831 without source code or debugger access. The final RCE development stage cost $535.74 in API fees over an 8-hour, 32-minute session (2.6k input and 1.3M output tokens) and required heavy researcher guidance. Claude produced working ICMP and UDP beacon exploits in 12 minutes after identifying that normal FTP command completion was overwriting its shellcode, fixed by omitting the CRLF terminator. A follow-up session extending the payloads toward a C2 implant wrote to flash-mapped memory and permanently bricked the PLC, underscoring the risks of autonomous agents operating against physical infrastructure.

Security Affairs·Sep 2, 13:50 UTC · 14d agoResearchCVE-2021-318861

Mapping out your unknown: A threat hunter’s guide to GitHub

Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.

Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.

Datadog Security Labs·Sep 16, 00:00 UTC · 1d agoResearch in the wild1

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 · 20d agoAI safety & security in the wild2

A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

Princeton researcher Yifan Zhang proposes Recurrent Looped Transformer, carrying full decoder state across every token for unbounded temporal depth.

Yifan Zhang's technical report defines the Recurrent Looped Transformer (RLT), pairing a causal encoder with a recurrent decoder whose final output and layerwise sliding-window attention cache carry into every subsequent token with no prompt-response boundary reset. The reference configuration ties 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t blocks after t tokens at fixed per-token compute. The report details RL replay contracts that rebuild all states under current parameters and exact prefix snapshots for multi-turn serving, but explicitly reports no measured efficiency, reasoning quality, or scaling results.

MarkTechPost·Sep 13, 17:02 UTC · 3d agoAI research1

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

Honeypot-Omaha and batch.py [Guest Diary], (Wed, Sep 2nd)

A SANS ISC guest diary describes batch.py, a Python tool that consolidates honeypot logs and enriches IOCs with threat intelligence data.

Written by a SANS.edu BACS intern, the diary explains analysis of the DShield Honeypot-Omaha sensor, which uses Cowrie to emulate SSH and Telnet and log attacker activity. The author's batch.py script implements a four-phase pipeline with SHA-256-generated master and guest authentication to consolidate JSON and log files, correlate data via external APIs, and produce MITRE, CVE, geolocation, threat-score and fingerprint enrichment for investigated indicators.

SANS Internet Storm Center·Sep 3, 02:02 UTC · 14d agoTools1

Quoting Rick Brewster

Paint.NET added a clean-room Direct2D rewrite for WINE, largely written by Anthropic's Claude and described as unreviewed 'vibe coded' code.

Rick Brewster says Paint.NET now ships a from-scratch, reverse-engineered Direct2D implementation (PaintDotNet.Windows.Direct2D1.Managed.dll) used under WINE via a /wine flag, since Direct2D was never completed well enough there. He credits the Claude coding assistant with writing most of the code, calling it largely 'vibe coded' and not thoroughly reviewed. Simon Willison shared the quote as an example of shipping AI-assisted systems code in production software.

Simon Willison·Sep 2, 05:50 UTC · 14d agoAI tools & infra1

Untracked Nightmares: The Threats Hiding Behind Commodity Infrastructure

Unit 42 exposes CL-CRI-1171, a pay-per-install network spreading malware like Insomnia RAT via YouTube channels and SEO poisoning for over two years.

Palo Alto Networks Unit 42 details CL-CRI-1171, a cybercrime cluster operating a pay-per-install (PPI) marketplace that has delivered multiple malware families for at least two years. The group used at least eleven YouTube gaming channels with hundreds of thousands of followers, plus SEO poisoning promoting trojanized software such as a Bluetooth driver and WinDirStat, infecting gamers and corporate endpoints including critical infrastructure and government entities. A single shared loader delivered payloads including Insomnia RAT, ARKTunnel, Docro Hijacker, GCleaner and Socks5Systemz between July 2025 and April 2026, with more than 10,000 distinct loader samples and over 200 rotating C2 domains identified. YouTube terminated the malicious channels after Unit 42 notified the platform.

Palo Alto Unit 42·Sep 9, 10:00 UTC · 7d agoMalware in the wild1

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

AsyncRAT Malware Abuses AutoIt and PowerShell to Hide Inside Legitimate Windows Process

Point Wild details a five-stage AsyncRAT campaign using AutoIt abuse and process injection to hide a .NET RAT inside Microsoft's signed charmap.exe.

Researchers at Point Wild Threat Intelligence analyzed a campaign starting from a lure batch file ('Right-click to open Invoice Details.bat') that launches hidden PowerShell to reconstruct a Base64/XOR-obfuscated payload. The stage drops a renamed signed AutoIt interpreter and loader script into %LOCALAPPDATA%\Temp and achieves persistence via a batch file in the user's Startup folder (T1547.001). The loader decrypts the payload in memory with single-byte XOR key 0x36 and injects it into SysWOW64\charmap.exe using OpenProcess/VirtualAllocEx/WriteProcessMemory/CreateRemoteThread, with PE-sieve confirming an in-memory implanted PE and patched AMSI modules. The final AsyncRAT payload performs screen capture and data theft, with observed C2 at 158[.]51[.]122[.]136:4944 over raw TCP.

GBHackers·Sep 14, 05:24 UTC · 2d agoMalware3· 1 read

More than half of AI

CyberScoop·Aug 7, 17:10 UTC · Aug 7, 2026Exploit / PoC

Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training

NVIDIA researchers detail an end-to-end system for online draft co-training that speeds speculative decoding in large-scale long-context RL post-training.

The paper tackles scaling online draft co-training for speculative decoding in RL post-training, where rollout generation dominates cost. It extends packed, load-balanced zigzag ring attention to merge rank-local branch attention with causal main-sequence attention for context parallelism, and introduces TapChannel to transport target features across pipeline-parallel stages without changing the schedule. Experiments show co-trained drafts tracking the policy baseline with substantial rollout and end-to-end speedups up to 122B parameters and strong scaling at 256K tokens.

Hugging Face daily papers·Sep 6, 20:00 UTC · 10d agoAI research

Nebulon Enterprise Simulated Threats for Phishing Research (NEST-Phish): A Synthetic Enterprise Phishing Email Dataset for Behavioral and Machine-Learning Research

Researchers release NEST-Phish, a synthetic enterprise phishing email dataset with matched legitimate and phishing emails and cue annotations for detection research.

Academic researchers introduce NEST-Phish, a publicly released synthetic enterprise phishing email dataset built around a fictitious organization named Nebulon. It contains matched synthetic legitimate and phishing emails across a broad set of workplace communication themes, each with interpretable phishing-cue annotations. Human-subject categorizations and supervised classifier evaluations indicate the dataset supports meaningful variation in phishing judgments and provides learnable signal for detection models. It is intended to support work on phishing detection, human susceptibility, explainability, and benchmark development.

arXiv cs.CR·Sep 3, 20:58 UTC · 13d agoResearch