AI Security Testing
Offensive testing for ML systems: training pipelines, model interfaces, and the integrations that make AI risky in production.
Offensive testing for AI and ML systems, including model and pipeline security.
Why teams engage us
AI features ship fast; security often lags behind data science velocity. Risks span data poisoning, pipeline tampering, model theft, insecure integrations, and unsafe autonomy in agents. Traditional AppSec playbooks only cover part of the surface.
What we deliver
We assess AI/ML systems end-to-end: data ingestion and labeling controls, training and deployment pipelines, model serving interfaces, and downstream application integrations. Testing draws on evolving community guidance (including OWASP ML Top 10 themes) and practical attacker models. You receive findings prioritized by exploitability and business impact, not abstract ML theory.
How we run it
- Architecture review: data flows, trust boundaries, and autonomy
- Threat modeling for AI-specific abuse cases
- Targeted technical testing with safe boundaries
- Controls mapping: preventive, detective, and governance
- Remediation workshop with ML and security owners
Outcomes you can expect
- Documented risks across data, model, and serving layers
- Hardening guidance for pipelines, access control, and monitoring
- Safer release criteria for new models and features
- Executive narrative on AI risk suitable for governance forums
Why Evaluris
- •Research-driven AI security practice paired with offensive tradecraft.
- •We speak to both ML engineers and security stakeholders.
- •Assessment scope can include agentic workflows and tool-use surfaces when applicable.
Deliverables
- AI security report
- Hardening recommendations
- Best practices
When it makes sense
- •Production AI/ML rollout
- •Regulatory or customer requirements
- •High-risk AI use cases
FAQ
Do you need our training data?
Usually not in raw form. We work from architecture, samples, and non-production environments when possible. Data handling is defined contractually.
Third-party models and APIs?
We evaluate how you integrate external models and secrets handling, within provider constraints and legal boundaries.
Is this red teaming for AI?
It can include adversarial-style testing, but the engagement is tailored, sometimes emphasis is pipeline integrity or access control rather than model-only attacks.
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ViewReady to scope this engagement?
Tell us about your environment, timelines, and objectives, we will respond with a tailored proposal.