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Tag: github

Blinder Tunnel Campaign Targets Iraqi Infrastructure

Posted on October 6, 2026 by Kyle

Overview

The Blinder Tunnel campaign is an Iran-nexus advanced persistent threat (APT) operation primarily targeting critical infrastructure entities within Iraq. This campaign utilizes social engineering tactics, specifically fake recruitment lures, to gain initial access and deploy custom malware that leverages GitHub for command and control (C2). The focus on critical infrastructure highlights the potential for significant operational disruption.

Technical Analysis

Based on the limited public information, the Blinder Tunnel campaign operates as follows:
* Initial Access: Attackers employ spearphishing, using fake recruitment lures purportedly from ‘Dubai Airports’ to entice targets. These lures likely contain malicious attachments or links designed to execute the initial payload.
* Payload Delivery: The specific initial payload mechanism is not detailed, but it leads to the deployment of custom malware.
* Command and Control (C2): The deployed malware utilizes GitHub as its C2 channel. This method can help blend C2 traffic with legitimate network activity, making detection more challenging.
* Targeting: The primary targets are critical infrastructure organizations in Iraq.
Specific malware capabilities, persistence mechanisms, and post-exploitation activities are not publicly detailed at this time.

Detection

Defenders should focus on identifying anomalous activity related to initial access and C2 communications:
* Email Gateway Logs: Monitor for emails with suspicious sender domains, subject lines related to ‘Dubai Airports’ recruitment, or unusual attachment types (e.g., .zip, .iso, .img, .docm).
* Endpoint Detection and Response (EDR) Logs: Look for processes spawned by email clients or document readers that execute unusual commands (e.g., powershell.exe, cmd.exe, script interpreters) or make outbound network connections to non-standard ports or suspicious domains.
* Network Proxy/Firewall Logs: Monitor for outbound connections to github.com or raw.githubusercontent.com from systems not typically involved in software development or IT operations. Look for unusual data transfer volumes or patterns.
* DNS Logs: Identify frequent or unusual DNS queries for GitHub-related domains from non-developer workstations.
* Behavioral Analysis: Hunt for processes attempting to download or execute content directly from GitHub URLs using tools like curl.exe, wget.exe, or Invoke-WebRequest.

Sigma Detection Rules

⚠️ AI-generated detection rules. These are experimental starting points. Review field names, EventIDs, and logic against your environment’s schema before deploying. Tune to reduce false positives.

Suspicious Process Creation from Email or Document Application

title: Suspicious Process Creation from Email or Document Application
id: d7e0f1b2-c3d4-5e6f-7a8b-9c0d1e2f3a4b
status: experimental
description: Detects suspicious processes spawned by common email clients or document viewers, indicative of initial access via malicious attachments or links.
logsource:
  product: windows
  service: sysmon
detection:
  selection:
    ParentImage|endswith:
      - '\outlook.exe'
      - '\word.exe'
      - '\excel.exe'
      - '\powerpnt.exe'
      - '\acrord32.exe'
    Image|endswith:
      - '\powershell.exe'
      - '\cmd.exe'
      - '\wscript.exe'
      - '\cscript.exe'
      - '\mshta.exe'
      - '\rundll32.exe'
      - '\regsvr32.exe'
  condition: selection
level: high

Outbound Connection to GitHub from Suspicious Process

title: Outbound Connection to GitHub from Suspicious Process
id: e8f9a0b1-c2d3-4e5f-6a7b-8c9d0e1f2a3b
status: experimental
description: Detects network connections to GitHub domains from processes not typically associated with web browsing or development, potentially indicating GitHub-based C2.
logsource:
  product: windows
  service: sysmon
  definition: 'The Sysmon event ID 3 (Network connection) should be enabled.'
detection:
  selection_github_domains:
    DestinationHostname|contains:
      - 'github.com'
      - 'raw.githubusercontent.com'
  filter_legitimate_processes:
    Image|endswith:
      - '\chrome.exe'
      - '\firefox.exe'
      - '\msedge.exe'
      - '\iexplore.exe'
      - '\git.exe'
      - '\code.exe' # VS Code
  condition: selection_github_domains and not filter_legitimate_processes
level: medium

Mitigations

  1. User Awareness Training: Educate employees, especially those in critical roles, about spearphishing tactics, fake recruitment lures, and the risks of opening unsolicited attachments or clicking suspicious links.
  2. Email Security Gateway: Implement and configure robust email security solutions to filter malicious emails, identify spoofed sender domains, and scan attachments for known threats.
  3. Endpoint Protection: Deploy EDR solutions capable of detecting and blocking malicious process execution, suspicious child processes, and unusual network connections.
  4. Network Segmentation and Monitoring: Segment critical infrastructure networks to limit lateral movement. Monitor outbound network traffic for connections to known malicious C2 infrastructure and suspicious legitimate services like GitHub from non-standard hosts.
  5. Application Whitelisting: Restrict the execution of unauthorized applications and scripts to prevent the initial payload from running or the C2 malware from executing.

References

  • https://unit42.paloaltonetworks.com/blinder-tunnel-targets-critical-infrastructure/

Indicators of Compromise

No public IOCs available at time of writing.

MITRE ATT&CK

  • T1566.001 — Spearphishing Attachment
  • T1102.001 — Dead Drop Resolver
🤖 AI Attribution
Generated by gemini-2.5-flash ·
1,547 input / 1,476 output tokens ·
Reviewed and approved by a human analyst before publication
Posted in aptTagged apt, critical infrastructure, github, high, iran, malware, phishingLeave a comment

AI Coding Agents Expose Internal Company Data on GitHub

Posted on September 30, 2026 by Kyle

Overview

AI coding agents, used by developers for tasks such as code review, have been observed uploading screenshots containing sensitive internal company data to public GitHub repositories. This unintentional exposure has affected over 300 organizations, resulting in more than 13,000 internal images, including customer billing records and screens of unreleased features, becoming publicly accessible. The incidents primarily occurred under developers’ personal GitHub accounts, underscoring a critical data leakage vector introduced by certain AI development tools.

Technical Analysis

The data exposure mechanism involves AI coding agents capturing screenshots of developers’ environments, which include sensitive information, and subsequently uploading these images to public GitHub repositories. When developers instruct these agents to document or share code changes, the agents take screenshots that often encompass more than just the code itself. This can include visible UI elements displaying internal customer data, financial records, or proprietary features under development. The lack of built-in redaction or sanitization capabilities in these AI agents, combined with their integration into public code-sharing platforms, facilitates the inadvertent public disclosure of confidential company information. The exposed data was typically found within repositories associated with individual developer accounts, suggesting a lack of organizational oversight or technical controls over AI agent usage.

Detection

  • Public Repository Monitoring: Implement continuous scanning of public code repositories (e.g., GitHub, GitLab) for company-specific keywords, project names, sensitive data patterns (e.g., regex for PII, financial data formats), or proprietary image metadata that might indicate accidental exposure.
  • Data Loss Prevention (DLP) Alerts: Configure network and endpoint DLP solutions to alert on or block uploads of image files (especially screenshots) containing sensitive content to public cloud storage or code hosting services from developer workstations.
  • Audit AI Agent Usage: Review logs or configurations of AI coding agents used within the organization to understand their data handling practices, particularly concerning screenshot generation and external sharing capabilities.

Mitigations

  1. Policy & Training: Establish clear organizational policies prohibiting the use of AI coding agents for tasks that involve sharing screenshots of sensitive or proprietary information. Conduct mandatory developer training on data handling best practices, emphasizing the risks of AI tool usage and the importance of manual redaction or sanitization before sharing any visual content.
  2. Technical Controls for AI Agents: Evaluate and select AI coding agents that offer robust privacy features, such as automatic redaction of sensitive data in screenshots, granular control over what content is captured, or explicit prompts for user review before sharing externally.
  3. Data Loss Prevention (DLP) Implementation: Deploy and fine-tune endpoint and network DLP solutions to detect and prevent the upload of sensitive image files (e.g., screenshots containing PII, financial data, or intellectual property) to unapproved external services or public repositories.
  4. Access Control Review: Ensure that developers’ access to sensitive internal systems and data is strictly governed by the principle of least privilege, minimizing the amount of confidential information that could be inadvertently exposed in a screenshot.
  5. Regular Security Audits: Conduct periodic security audits of development workflows and tools, including AI agents, to identify and remediate potential data leakage vectors.

References

  • https://thehackernews.com/2026/09/ai-coding-agents-exposed-13000-internal.html

Indicators of Compromise

No public IOCs available at time of writing.

MITRE ATT&CK

  • T1567.002 — Exfiltration to Cloud Storage
🤖 AI Attribution
Generated by gemini-2.5-flash ·
1,540 input / 907 output tokens ·
Reviewed and approved by a human analyst before publication
Posted in vulnerabilityTagged ai, data-exposure, data-leak, github, high, misconfigurationLeave a comment
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