• Home
  • C1PH3R-GH05T
    • Internal Pen Testing
    • External Pen Testing
    • Web & App Security
    • Mobile App/AI ML Security
    • Vulnerability Scanning
    • Red Teaming Assessment
    • Zero-day threats
  • About Us
    • Why choose us?
    • Methodologies & Standards
  • What to expect
  • Ready to talk?
  • More
    • Home
    • C1PH3R-GH05T
      • Internal Pen Testing
      • External Pen Testing
      • Web & App Security
      • Mobile App/AI ML Security
      • Vulnerability Scanning
      • Red Teaming Assessment
      • Zero-day threats
    • About Us
      • Why choose us?
      • Methodologies & Standards
    • What to expect
    • Ready to talk?
  • Home
  • C1PH3R-GH05T
    • Internal Pen Testing
    • External Pen Testing
    • Web & App Security
    • Mobile App/AI ML Security
    • Vulnerability Scanning
    • Red Teaming Assessment
    • Zero-day threats
  • About Us
    • Why choose us?
    • Methodologies & Standards
  • What to expect
  • Ready to talk?

Mobile Application and AI ML/LLM Pentesting security solutions network secu

  

 

 

Protecting modern attack surfaces requires testing beyond traditional apps and networks — we combine deep mobile-app expertise with specialized AI/ML assessment to find weaknesses in both code and models.


What we test — Mobile

  • iOS & Android static analysis (binary & source-level review where available)
     
  • Runtime and dynamic analysis (instrumentation, tampering, runtime manipulation)
     
  • API/backend security, authentication, session management and rate limits
     
  • Insecure data storage, key management, and secrets leakage (incl. keystores, Keychain, SharedPreferences)
     
  • Reverse-engineering resilience, code obfuscation checks, and dependency analysis
     
  • Client-side logic flaws, business-logic abuse, and privilege escalation paths
     
  • CI/CD / build pipeline review and app-signing integrity checks
     

What we test — AI/ML & LLMs

  • Threat modeling for model access and data flows (training ↔ inference ↔ storage)
     
  • Prompt-injection and input-sanitization testing against LLM interfaces and chat endpoints
     
  • Model extraction / theft risk analysis and watermarking feasibility
     
  • Data poisoning and training pipeline integrity review (process controls & provenance)
     
  • Privacy assessments for PII leakage from model outputs and logs (inference-time disclosure)
     
  • Adversarial robustness checks and adversarial example evaluation at a high level (defensive posture, not exploit recipes)
     
  • API hardening, rate-limiting, authentication, and telemetry for model APIs
     
  • MLOps / deployment hardening: secrets management, rollback controls, and monitoring
     

Deliverables

  • Executive summary tailored for leadership (risk, impact, prioritized actions)
     
  • Detailed technical report with findings, risk ratings, reproducible evidence, and safe PoC descriptions where appropriate
     
  • Actionable remediation guidance (short-term fixes + strategic improvements)
     
  • Post-remediation retest option and ongoing monitoring recommendations

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