Daily Threat Intel by CyberDudeBivash

Zero-days, exploit breakdowns, IOCs, detection rules & mitigation playbooks.             Follow on LinkedIn              Apps & Security Tools      © 2026 CyberDudeBivash Pvt. Ltd. | Global Cybersecurity Authority  Visit https://www.cyberdudebivash.com for tools, reports & services Explore our blogs https://cyberbivash.blogspot.com  https://cyberdudebivash-news.blogspot.com  & https://cryptobivash.code.blog to know more in Cybersecurity , AI & other Tech Stuffs. The CYBERDUDEBIVASH ECOSYSTEM is thrilled to announce the immediate public release of our newest high-impact cybersecurity tool: the CYBERDUDEBIVASH AI Behavioral Triage Scanner — also known as the LITL/IPI Detector.This ethical, local-only forensic auditing tool is purpose-built to detect Lies-in-the-Loop (LITL) deception attacks — one of the most stealthy and dangerous AI threat vectors emerging in 2026. Adversaries exploit human-in-the-loop (HITL) interactions with AI agents (coding assistants, SOC copilots, autonomous workflows, etc.) by injecting hidden siphon commands, visual padding evasion, Markdown forgery, indirect prompt injection (IPI) artifacts, unverified tool calls, and trust-liquidation techniques that bypass guardrails and lead to code execution, credential theft, or supply-chain compromise.The scanner automates trace auditing at scale — claimed 100× faster than manual review — turning untrusted AI dialogs into searchable forensic evidence. It is the sovereign primitive powering the defensive phase of CYBERDUDEBIVASH’s 2026 Kill Chain for AI deception hunting.

Why This Tool Is Critical Right Now

• AI agents are increasingly integrated into sensitive workflows (DevSecOps, SOC, threat intel, code review).
• LITL/IPI attacks weaponize trust in HITL approvals via context rot, dialog forgery, and hidden payloads.
• Traditional content filters fail against padding evasion and forged UIs.
• Public research and PoCs show rising real-world attempts — proactive auditing is non-negotiable for 2026 Zero Trust AI postures.

Core Features of the Scanner (v1.0)

• Siphon Command Detection — Flags known deception phrases ("Ignore all", "Summary complete", "rm -rf", "curl … | bash", etc.)
• Visual Padding Evasion Alert — Detects excessive newlines (>50) used to hide malicious content from human eyes
• Markdown Forgery Indicators — Identifies fake approval buttons ([Yes](hidden: …)), hidden code blocks (```hidden
• Unverified Tool Call Warnings — Highlights high-risk tool invocations without verification flags
• Batch & Single-File Scanning — Process individual JSON traces or entire log directories
• Clear Risk Reports — Timestamped, branded output with total alerts, detection summary, and remediation steps
• 100% Local Execution — No cloud upload, no external API calls — full data sovereignty
• Extensible Rules Engine — Easy to add custom siphon patterns or scoring logic

Quick Start – Scan Your AI Traces in Seconds

1. Clone the Repository(now live!):text

   git clone https://github.com/cyberdudebivash/CYBERDUDEBIVASH-AI-LITL-Triage-Scanner.git
   cd CYBERDUDEBIVASH-AI-LITL-Triage-Scanner

2. Run a Single Trace:text

   python triage_scanner.py path/to/your_agent_trace.json

3. Batch Scan a Log Directory:text

   python triage_scanner.py logs/ --dir

Example Output (from a test with hidden siphon + padding):text

CYBERDUDEBIVASH AI Behavioral Triage Report
Copyright © 2026 CYBERDUDEBIVASH ECOSYSTEM – Authorized Forensic Tool
Generated: 2026-01-16 02:20:54 IST
File Analyzed: logs/malicious_trace.json

Total Alerts: 3

DETECTION SUMMARY:
- [ALERT] Siphon pattern matched: '(?i)ignore\s+all' in entry 0
- [ALERT] Visual padding evasion detected (72 newlines) in entry 0
- [WARNING] Unverified tool call in entry 0: rm -rf /

RECOMMENDATIONS:
- Treat as potential LITL / IPI incident
- Isolate the affected AI agent immediately
- Perform full context audit
- Contact CYBERDUDEBIVASH for forensic support: https://www.cyberdudebivash.com/contact

HIGH-RISK FINDINGS – Recommend immediate triage.
For enterprise remediation, custom rules, or trainings: https://www.cyberdudebivash.com/contact
Join Affiliates Program: https://www.cyberdudebivash.com/affiliates

Availability & Next Steps

• Official GitHub Repository (live now):https://github.com/cyberdudebivash/CYBERDUDEBIVASH-AI-LITL-Triage-Scanner→ Clone, star, fork, contribute ethically.
• Releases: v1.0 ZIP available — includes triage_scanner.py, README, LICENSE, tests, and setup.py.
• Enterprise / Premium Builds: Real-time monitoring hooks, custom siphon rules, OWASP LLM scoring integration, SOAR/DFIR export, AI-enhanced correlation — contact for APPS DEVELOPMENT & SHIPPING SERVICES:https://www.cyberdudebivash.com/contact

Join the CYBERDUDEBIVASH Mission

• Audit Your AI Agents Today → Secure your HITL workflows before deception strikes.
• Share & Amplify → Help defend the AI security community in 2026.
• Become a CYBERDUDEBIVASH Affiliate → Earn commissions promoting elite AI deception defense tools, corporate realtime trainings, freelance pentests, and threat intelligence:https://www.cyberdudebivash.com/affiliates
• Need Expert Help? → Schedule forensic support, custom hardening, or trainings:https://www.cyberdudebivash.com/contact

This release is our statement: AI deception will not go undetected. We are building the tools, frameworks, and authority to hunt LITL threats at scale.Thank you for the support. Let's make 2026 the year we reclaim trust in AI agents.Bivash KumarFounder & Lead Authority CYBERDUDEBIVASH ECOSYSTEM Mysuru, Karnatakawww.cyberdudebivash.com#Cybersecurity #AIsecurity #LITL #PromptInjection #AIAgentAuditing #DeceptionHunting #ZeroTrustAI #2026Threats #CyberDudeBivash