Ransomware in 2026 is no longer about encrypting files and demanding Bitcoin. It has evolved into multi-layered extortion targeting AI systems, supply chains, and data integrity. Behnaz Karimi, a cybersecurity researcher and active contributor to the OWASP AI Exchange, specializes in ransomware attacks against AI systems. In this episode, she breaks down how attackers exploit AI pipelines with APT-level patience, why traditional incident response fails for poisoned models, and what organizations of every size must do to build resilience.
You can read the complete transcript of the episode here >
How has ransomware evolved in the AI era?
The traditional model of locking files and demanding cryptocurrency is becoming outdated. Modern ransomware in 2025-2026 operates on multiple fronts:
- Data theft over encryption: Many groups no longer encrypt systems. They steal sensitive data and apply pressure through legal threats, compliance exposure, and reputation damage. Even strong backups do not protect against this.
- Multi-layered extortion: Attackers launch DDoS attacks, contact customers and partners directly, and even threaten to report companies to regulators.
- One-to-many attack model: Instead of targeting individual companies, attackers compromise managed service providers to hit thousands of organizations simultaneously.
- Insider recruitment: Ransomware groups increasingly recruit insiders to facilitate access, making technical perimeter defenses insufficient.
- AI as a weapon: Autonomous AI agents can automate exploitation, analyze stolen data, and handle ransom negotiations. Even small groups can now launch large-scale attacks.
Ransomware is no longer just a technical problem. It is a social and organizational threat that requires equally broad defenses.
What are the common attack patterns against AI systems?
Behnaz identifies a three-act pattern that repeats across AI-targeted ransomware:
- Act 1 — Supply chain infiltration: An attacker uploads a poisoned model to a public repository. It passes benchmarks because it was engineered to stay dormant during testing. Teams pull it in without suspecting anything.
- Act 2 — Pipeline persistence: The malicious logic does not sit on a server waiting. It weaves into ML pipelines, training preprocessing hooks, and model checkpoints. It waits 72 hours or more, long enough to clear anomaly detection windows. This is APT-level patience.
- Act 3 — Surgical detonation: Checkpoints encrypted, inference endpoints down. The ransom note surfaces through the organization’s own API as error responses in their own dashboard. The AI delivers its own extortion message.
The most terrifying part: recovery is not a restore from backup. It requires full pipeline reconstruction and complete retraining from scratch, taking days or months, not hours. Every transaction in that window carries risk. This makes AI security posture management critical for organizations deploying AI at scale.
Why is the model repository the most overlooked entry point?
While security teams focus on perimeters, firewalls, and endpoint detection, attackers walk straight through the AI supply chain. The most dangerous entry points:
- Public model repositories: Pre-trained models from Hugging Face or similar platforms that pass benchmarks but contain dormant malicious logic.
- ML framework dependencies: Third-party libraries with injected code that activates under specific conditions.
- GenAI prompt interfaces: Sensitive data exposure through unmonitored prompt interactions.
The gap exists because:
- Capability gap: Teams lack tools to monitor AI systems or validate what enters their pipelines. Traditional security controls were not built for this.
- Visibility problem: AI systems retrain automatically, rely on many dependencies, and often lack security baselines. When something goes wrong, early detection is nearly impossible.
- Governance gap: Most organizations do not track where models come from or validate them properly. This mirrors the early days of open source software before SCA tools became standard.
Organizations that want to address this gap should explore agentic AI security frameworks that cover these novel attack surfaces.
Are small and mid-sized organizations safe from AI ransomware?
No. The myth that smaller organizations are not worth targeting is dangerous. Several factors make them more vulnerable:
- Ransomware-as-a-service: Powerful attack tools are available to anyone. Attacks that once required sophisticated groups can now be launched by individuals with minimal technical skills.
- Faster AI adoption, weaker controls: Small companies adopt AI tools (third-party APIs, co-pilot, ChatGPT) just as fast as large enterprises but without equivalent security controls.
- No model tracking: Smaller organizations rarely monitor where models come from or what enters their systems.
- MSP attacks: Attackers target service providers to compromise thousands of small organizations simultaneously.
Attack tools are becoming accessible faster than defensive measures. A small healthcare company using AI for patient data has the same data value as a large one but far fewer resources to detect and respond to compromise.
Why does traditional incident response fail for AI systems?
Traditional ransomware recovery is straightforward: isolate, remove malware, restore from backup. For AI systems, this approach is fundamentally insufficient:
- Models are not files: An AI model is built from millions of parameters shaped by training data. You cannot simply “restore” it like a database.
- Poisoning is invisible: Attackers can quietly damage the model itself without encrypting anything. The model may have been producing compromised outputs for weeks before the overt attack.
- Backup restoration is risky: If the model was poisoned before your backup point, restoring from backup restores the poison.
What AI incident response requires instead:
- Deep verification: Track model provenance and use techniques to confirm models have not been changed. Without this, you are guessing.
- Extended investigation: Beyond logs and network activity, examine how the model was trained, what dependencies it used, and how it has been behaving over time.
- Retrain from scratch when uncertain: If you are not fully confident the model is clean, do not restore it. Retrain in a clean environment using trusted data. This is expensive but is the only way to guarantee integrity.
- Output monitoring: Continuously monitor model outputs for anomalous behavior that could indicate poisoning. This is the only way to detect attacks that bypass traditional security controls.
This represents a fundamental shift in incident response methodology that most teams are not yet prepared for.
How should organizations assess their AI ransomware resilience?
Behnaz recommends starting with honest self-assessment through simple questions:
- Can a normal IT attack reach our AI systems? If yes, the blast radius of any breach now includes AI.
- If an AI component is compromised, can it affect the rest of our IT environment? Understand the interconnections.
- Do we only use trusted models? Can models run code in our environment? If something is compromised, can it spread? Do we have clean backups?
- Do we know what models and ML libraries are running in production and where they came from? If not, that is a supply chain risk.
The OWASP AI Exchange framework recommends: build an inventory of AI tools and use cases, identify risks that actually apply to you, assess likelihood and impact, and continuously review as systems evolve. Any change to AI systems requires re-evaluating the security posture.
The bottom line: invest in security controls now rather than paying attackers later. Basic security hygiene for AI systems, model validation, supply chain monitoring, output governance, is not optional. It is the difference between business continuity and catastrophic loss.
