Scientific LM red team
Adversarial testing of scientific language models and evaluation harnesses used in research workflows.
Scientific Red Team
Adversarial assurance for scientific A.I.
Tortzal stress-tests scientific language models, co-scientist agents, and high-sensitivity bio/chem pipelines before adversaries or regulators do.
Scientific A.I. under adversarial scrutiny
Co-scientist and scientific LM systems on bio and chem data carry distinct failure modes. Tortzal maps attack surfaces across evaluation harnesses, agent toolchains, and sensitive pipelines, with findings structured for security, compliance, and scientific stakeholders.
Capabilities
Co-scientist agent testing
Red team exercises against agentic co-scientist systems, tool access, and autonomous research workflows.
Bio & chem data pipelines
Assurance for pipelines handling high-sensitivity biological, chemical, and export-controlled datasets.
Evidence-ready reporting
Findings structured for security leadership, compliance reviewers, and scientific stakeholders.
Engagement model
- 01
Scope systems, data classes, and compliance constraints.
- 02
Execute adversarial scenarios against pipelines and controls.
- 03
Deliver prioritized remediation paths with reproducible evidence.
Deliverables
What you receive
Evidence-grade outputs for security leadership, compliance reviewers, and scientific stakeholders.
Executive findings brief
Decision-ready summary of adversarial results, risk posture, and recommended next steps.
Attack scenario catalog
Documented adversarial scenarios against scientific LMs, agents, and data pipelines with reproduction steps.
Prioritized remediation roadmap
Ranked control and architecture fixes mapped to compliance constraints and operational impact.
Reproducible evidence package
Artifacts, logs, and test harness outputs structured for audit, legal review, and internal security teams.
Contact
Start an engagement
Tell us about your organization, systems in scope, and timeline. We respond within two business days.