Service
Proactively uncover and remediate critical vulnerabilities in your LLMs and generative models before attackers exploit them.
Generative AI introduces novel risks that traditional security tools miss. Our adversarial testing simulates real-world attacks to expose critical flaws like prompt injection, jailbreaking, and training data leakage.
We identify vulnerabilities that could lead to data breaches, compliance failures, or reputational damage, providing actionable remediation to secure your AI investments.
Our methodology is built on proven frameworks like MITRE ATLAS and includes:
We deliver more than a report. You receive:
Secure your AI innovation. Protect against evolving threats with expert-led penetration testing. Explore our broader AI Red Teaming and Adversarial Defense services or learn about securing autonomous systems with AI Agent Goal Hijacking Defense.
Our penetration testing engagements deliver more than a report. We provide actionable, prioritized remediation roadmaps and verifiable security improvements that directly reduce risk and protect your AI investment.
Receive a detailed, actionable report with CVSS-scored vulnerabilities, step-by-step remediation guidance, and a clear timeline for patching critical issues like prompt injection and data leakage.
Our testing methodology is mapped to the MITRE ATLAS framework, providing a standardized, evidence-based view of your AI security posture that satisfies internal audit and regulatory scrutiny.
We don't just describe vulnerabilities; we demonstrate them with safe, controlled proof-of-concept attacks. This eliminates ambiguity for your engineering team and accelerates fix deployment.
Beyond the report, we offer optional retesting and consultation to validate fixes, implement defensive guardrails, and help establish a continuous AI red teaming program. Learn more about our Continuous AI Red Teaming Programs.
Our testing identifies paths for model extraction, training data inversion, and sensitive information leakage—direct threats to your core intellectual property and customer privacy.
We test for emerging threats beyond traditional IT security, including jailbreaking, adversarial examples for multimodal models, and supply chain attacks on model weights. Explore related services like AI Supply Chain Security Assessment.
Our structured penetration testing engagements are designed to uncover critical vulnerabilities in your generative AI systems, from foundational assessments to continuous security programs.
| Security Assessment | Starter | Professional | Enterprise |
|---|---|---|---|
Core MITRE ATLAS Framework Testing | |||
Prompt Injection & Jailbreak Testing | |||
Data Leakage & Model Inversion Testing | |||
Adversarial Example (Evasion) Testing | |||
RAG System & Vector DB Manipulation Testing | |||
AI Agent Goal Hijacking Assessment | |||
Physical AI / Robotics Interface Testing | |||
Remediation Guidance & Technical Report | Summary | Detailed | Detailed + Workshop |
Testing Timeline | 2-3 weeks | 4-6 weeks | 8+ weeks or Continuous |
Starting Investment | $25K | $75K | Custom |
Our Generative AI Penetration Testing services are tailored to secure high-value AI applications across regulated and high-risk sectors. We identify novel vulnerabilities before they impact your operations, revenue, or compliance posture.
Protect algorithmic trading models, fraud detection AI, and customer-facing chatbots from prompt injection and data leakage that could lead to market manipulation or regulatory fines. Our testing aligns with FFIEC and GDPR requirements for AI systems.
Secure clinical decision support systems, ambient documentation AI, and drug discovery models against manipulation that could compromise patient safety or violate HIPAA. We test for data poisoning in training pipelines and hallucination risks in diagnostic outputs.
Harden contract analysis AI, litigation prediction models, and compliance copilots against jailbreaks that could generate incorrect legal advice or leak privileged client data. Our assessments ensure adherence to attorney-client privilege and bar ethics rules.
Conduct adversarial testing on geospatial intelligence AI, secure communication models, and autonomous systems for resilience in contested environments. Our testing includes physical AI and robotics security red teaming for safety-critical failures.
Secure hyper-personalization engines, dynamic pricing AI, and multimodal customer support bots from manipulation that could distort recommendations, enable fraud, or damage brand reputation through harmful outputs.
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.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Common questions from CTOs and security leads about our adversarial testing methodology, timelines, and outcomes for generative AI systems.
Our methodology is based on the MITRE ATLAS framework, adapted for generative AI. We conduct systematic adversarial attacks across five key vectors: prompt injection, jailbreaking, data leakage, model manipulation, and supply chain risks. Each engagement includes threat modeling, automated and manual exploitation, and a detailed remediation report with prioritized fixes. We've secured over 50+ AI models across finance, healthcare, and enterprise SaaS.
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.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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