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Vulnerability Research & Experiments (From JMak-Security)

Welcome to my security research repository. This space serves as an ongoing technical journal and portfolio where I design, build, and analyze self-contained laboratories to evaluate cryptographic implementations, software logic flaws, and advanced red-team exploit vectors.

The objective of this repository is to demonstrate practical cybersecurity concepts, moving past theoretical definitions into fully functional proof-of-concept (PoC) code.


📂 Laboratory Directory

🧪 01. Post-Quantum Cryptography (PQC) Side-Channel Bypass (2026-06-02)

  • Directory Location: /PQC-SideChannel-Bypass
  • Primary Concepts: ML-KEM-768, AES-GCM-256, CWE-532 (Insecure Logging), Cryptographic Bypass.
  • Core Vulnerability: A hybrid post-quantum data pipeline is completely neutralized due to an administrative logging flaw that leaks ephemeral symmetric key material to a plaintext file.
  • Key Takeaway: Algorithmic strength (even NIST-standardized quantum defenses) cannot compensate for insecure implementation logic.

🧪 02. Semantic Logic Hijacking & Multi-File Context Manipulation (2026-06-26)

  • Directory Location: /2026-06-26 - Semantic Hijacking IPI Lab for PISA
  • Primary Concepts: Indirect Prompt Injection (IPI), Context Window Unification, Compliance Bias, Fragmented Exfiltration.
  • Core Vulnerability: An LLM agent reconstructs functional exfiltration strings from fragmented inputs across separate files, bypassing static signature filters.
  • Key Takeaway: Structural instruction adherence can override data safety parameters when context is unified across disparate sources.

🧪 03. Multi-Stage AI & Traditional Infrastructure Homelab (2026-07-03)

  • Directory Location: /2026-07-03 - AI & Infra-Homelab
  • Primary Concepts: CWE-78 (Command Injection), CWE-918 (SSRF), CWE-502 (Insecure Deserialization), Docker Socket Breakout, Network Segmentation.
  • Core Vulnerability: A three-tier AI microservice stack with isolated Docker networks is fully compromised through chained traditional attacks—from perimeter injection to host escape via a mounted Docker daemon socket.
  • Key Takeaway: AI-adjacent infrastructure inherits classical web and container flaws; network segmentation alone cannot stop dual-homed SSRF pivoting.

🧪 04. IMDSv2 SSRF & AI Training Data Poisoning Lab (2026-07-09)

  • Directory Location: /2026-07-09 - IMDSv2 SSRF & AI Training Data Poisoning Lab
  • Primary Concepts: Cloud storage IAM misconfiguration, IMDSv2 credential theft (CWE-918 + CWE-532), AI training-data poisoning via leaked session tokens.
  • Core Vulnerability: A dual-homed FastAPI AI gateway trusts attacker-controlled storage_path values with an over-privileged role, leaks temporary IAM credentials through an SSRF→IMDS diagnostics probe, and enables a secondary cloud pivot that overwrites secure fine-tuning corpora.
  • Key Takeaway: AI data planes fail the same way cloud control planes do — over-privileged roles, metadata exposure, and unauthenticated object writes compound into full pipeline compromise without a single classical RCE.

More laboratory experiments added continuously as research progresses...


🛡️ Responsible Disclosure & Usage Policy

The labs and source code provided across this repository are created strictly for educational, research, and defensive compilation purposes.

  • All target systems and architectural flaws are simulated locally in controlled environments.
  • The techniques illustrated are designed to help security engineers, developers, and researchers understand implementation vulnerabilities to build more resilient software development lifecycles (SDLC).

Maintained by: JMak-Security

Continuous learning through breaking, fixing, and securing.

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Vulnerability Researches and Experiments for fun and learning

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