Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Tailored adversarial testing for custom, fine-tuned language models in high-stakes industries.
Your fine-tuned LLM for healthcare, finance, or legal services introduces novel attack surfaces. Generic security tools miss domain-specific jailbreaks, compliance violations, and specialized prompt injection techniques that could leak PHI, manipulate financial advice, or produce legally negligent outputs.
We simulate real-world adversaries to find vulnerabilities before they cause regulatory fines, reputational damage, or operational disruption.
Move beyond generic scans. Secure the unique risks in your custom AI. Explore our broader AI Red Teaming and Adversarial Defense pillar or learn about protecting autonomous systems with AI Agent Goal Hijacking Defense.
Our domain-specific red teaming delivers measurable security hardening, directly reducing your AI's attack surface and compliance risk. We provide actionable reports, not just findings.
We uncover and document exploitable security flaws in your custom LLM, including domain-specific jailbreaks, data leakage paths, and compliance violations, with proof-of-concept exploits.
Receive a security-hardened version of your model with mitigations implemented for discovered vulnerabilities, significantly raising the cost for real-world adversaries.
Generate defensible artifacts demonstrating due diligence for regulations like the EU AI Act, NIST AI RMF, and ISO/IEC 42001, turning security testing into a compliance asset.
Your engineers and security staff gain hands-on understanding of AI-specific threats through our debrief sessions, building long-term internal defensive capabilities.
Our engagement establishes a baseline and repeatable testing methodology, enabling you to integrate AI red teaming into your SDLC and consider our Continuous AI Red Teaming Programs for ongoing protection.
Our Domain-Specific LLM Security Red Teaming service is delivered through structured engagement tiers, each designed to match your model's criticality and compliance requirements. This table outlines the scope, deliverables, and support levels for each package.
| Security Assessment Component | Essential Audit | Comprehensive Red Team | Enterprise Resilience Program |
|---|---|---|---|
Initial Threat Modeling & Scoping | |||
Domain-Specific Jailbreak Testing (e.g., HIPAA, FINRA) | 50+ crafted attacks | 200+ crafted attacks | 500+ crafted & adaptive attacks |
Specialized Prompt Injection Testing | Core techniques | Advanced & chained techniques | Novel, research-grade techniques |
Compliance Violation Simulation (GDPR, EU AI Act) | Basic checks | Detailed scenario testing | Full adversarial compliance audit |
Adversarial Data Poisoning Assessment | |||
Model Extraction & Inversion Attack Testing | |||
Remediation Guidance & Technical Report | Summary report | Detailed report with PoC code | Prioritized roadmap & engineer briefing |
Retesting of Critical Vulnerabilities | 1 retest cycle | Quarterly retest cycles | |
Ongoing Threat Intelligence & Advisories | Monthly briefings & CVE monitoring | ||
Dedicated Security Engineer Support | Priority Slack Channel | Named Technical Account Manager | |
Typical Engagement Timeline | 2-3 weeks | 4-6 weeks | Ongoing program |
Starting Investment | From $15,000 | From $45,000 | Custom annual contract |
Our red team combines deep adversarial expertise with your domain's specific risks. We don't just run generic tests; we simulate real-world, motivated attackers targeting the unique compliance, operational, and reputational vulnerabilities of your fine-tuned models.
We engineer and execute sophisticated prompts designed to bypass your model's specialized safeguards, testing for compliance violations, data leakage, and harmful outputs unique to your industry's context and terminology.
Beyond basic injection, we test complex multi-step attacks that manipulate your model's reasoning chain, exploit its fine-tuned knowledge, and corrupt its outputs within high-stakes workflows like legal analysis or financial reporting.
We proactively test for scenarios where your model could violate HIPAA, FINRA, GDPR, or other critical regulations, identifying data handling flaws and output risks before they result in penalties or legal exposure.
We audit your training pipeline and fine-tuning datasets for vulnerabilities to poisoning attacks that could embed backdoors or bias, ensuring the integrity of your domain-specific model's foundational knowledge.
We simulate advanced model extraction attacks to assess how easily a proprietary, fine-tuned model's weights or behavior can be stolen via API queries, protecting your significant R&D investment. Learn more about our broader Model Extraction and Inversion Attack Prevention services.
Receive clear, prioritized findings with reproducible attack code and direct remediation guidance your engineering team can implement immediately, reducing mean time to remediation (MTTR). This complements our ongoing Continuous AI Red Teaming Programs.
Secure clinical decision support and ambient AI against domain-specific threats. Our red teaming uncovers vulnerabilities in systems handling Protected Health Information (PHI) and medical logic, preventing compliance violations and protecting patient safety.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get answers to common questions about our tailored adversarial testing for fine-tuned language models in regulated industries.
A standard engagement takes 3-5 weeks from kickoff to final report. This includes 1-2 weeks for scoping and threat modeling, 2-3 weeks for active adversarial testing using frameworks like MITRE ATLAS, and 1 week for analysis and remediation guidance. For complex models with multiple specialized agents or extensive knowledge bases, timelines may extend to 6-8 weeks.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
