Introduction:
AI and ML systems are transforming industries—but they’re also creating new security challenges. Adversarial machine learning is no longer theoretical. Attackers are actively exploiting ML models in production. Here’s how to defend your AI investments.
What Are Adversarial Attacks?
These are intentional manipulations of input data to mislead a machine learning model.
- Evasion attacks: Modify inputs slightly to fool the model (e.g., make a stop sign look like a yield sign).
- Model inversion: Recover sensitive data used in training by querying the model.
- Data poisoning: Inject malicious data into the training set to corrupt future outputs.
Why It Matters:
If you’re using AI in finance, healthcare, or cybersecurity, these attacks can:
- Lead to wrong decisions
- Expose personal data
- Damage brand reputation
Defensive Techniques:
- Input validation and normalization
- Robust model training using adversarial examples
- Limiting model output to reduce leakage
- Monitoring model predictions for anomalies
Conclusion:
ML models must be treated like software, with threat modeling, red teaming, and secure lifecycle development. TrustStack offers AI/ML-specific assessments to test for adversarial risk and secure your models before they go live.
