By: Kaushik Shanadi, CTO

Model poisoning is becoming a practical concern as enterprises fine-tune and retrain models on proprietary and customer data. As training pipelines expand and update more frequently, adversaries have more opportunity to influence model behavior by manipulating the data and processes that shape learning.

The most important point for CISOs is that poisoning is typically a shaping attack, not a breaking one. A poisoned model can look healthy in standard evaluations and still meet performance expectations. The impact often appears in targeted situations such as specific prompts, edge-case workflows, or high-impact decisions where small changes in model behavior can create disproportionate business or security outcomes.

In practice, poisoning risk concentrates in three areas

Data provenance and supply chain exposure. Training and fine-tuning datasets are increasingly assembled from multiple internal systems, customer-driven inputs, third-party sources, and telemetry from live usage. Any channel that allows adversarial influence, even at low volume, can introduce patterns that the model learns and amplifies over time.

Retraining automation. As organizations move toward continuous learning and frequent model updates, the window for review shrinks. Attackers benefit from repetition and scale, while defenders rely on sampling and periodic validation. If poisoned signals are introduced gradually, they can evade manual review and become normalized in the training set.

Evaluation that is not designed to detect adversarial intent. Traditional metrics focus on aggregate accuracy and general performance. They do not reliably reveal targeted backdoors, behavior shifts tied to narrow trigger conditions, or subtle degradation that only appears in specific classes of inputs. Without testing aligned to plausible attack strategies, poisoning can persist unnoticed.

What this means for security leaders

CISOs should treat training as a production-grade security surface. That includes:

• Enforcing provenance and integrity controls on training data
• Monitoring for anomalous patterns in both ingestion and gradient behavior
• Setting policies for what data can influence retraining and when
• Implementing adversarial evaluation that looks for targeted behavior change rather than only performance regression

Model poisoning turns the training pipeline into a control point for attackers. Securing that pipeline is how organizations preserve trust in downstream applications that depend on model outputs.