Over the last 18+ months, I have watched a persistent threat actor use sustained DDoS activity and noisy…
Ghost Candidate
Over the last 18+ months, I have watched a persistent threat actor use sustained DDoS activity and noisy traffic to change how inline, AI‑driven security appliances treat my network.
At the beginning, the anomaly detection looked effective. Over time, I started to see something different. Legitimate external and internal requests, including hairpin routes, were degraded or blocked, while hostile patterns increasingly fell inside what the appliance treated as “normal.” In other words, the learning process itself seemed to be getting corrupted.
We talk a lot about offensive AI and defensive AI. I do not see enough discussion about long‑term attacks that target the learning mechanisms in these tools. Research on data poisoning and adversarial machine learning suggests that attackers can manipulate training or baseline data so that models misclassify threats as benign. If this can happen over months or years, the attacker is not just bypassing controls. They are changing how your defenses think.
For anyone running self‑learning IDS, NGFW, or behavior‑based controls in production:
Are you seeing baseline drift that you suspect is being influenced by hostile traffic?
How do you verify that your models and baselines have not been quietly poisoned by the environments they monitor?
What guardrails around training data, retraining, and human review have actually worked in practice?
These are the AI risks that keep me up at night. I would really like to hear how others are seeing this problem and what has worked to contain it.
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Liam R.: The failure mode is the one you've hit is where the defensive model is being slowly retrained by the very environment it's meant to police. That's not a bypass. It's a corruption of the model's sense of normal, and it's arguably the central unsolved risk in self-learning security tooling. Risks I'd flag for anyone relying on these systems: - First, drift is silent by design, because the model has