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