Secure AI &
LLM Security
The attack surface of AI is new, rapidly expanding, and largely undefended. We protect models, agents, pipelines, and the businesses that depend on them — with adversarial research that goes where conventional security cannot.
AI Security at the Intersection of ML and Adversarial Research
AI-Native Security Research
Our AI security practice is staffed by engineers who understand transformer architectures, fine-tuning pipelines, and embedding models from first principles — not security generalists who have read a prompt injection blog post. We test AI systems the way ML engineers build them: with deep knowledge of the internals.
OWASP LLM Top 10 Coverage
Our LLM security testing methodology provides complete coverage of the OWASP LLM Application Security Top 10 — the definitive framework for GenAI vulnerability classification.Every engagement produces findings mapped to this standard, enabling direct comparison against industry benchmarks and regulatory expectations.
MITRE ATLAS Framework
We apply the MITRE ATLAS (Adversarial Threat Landscape for AI Systems) framework to every engagement — the AI security equivalent of the ATT&CK framework for traditional cyber threats. Every attack technique we test is mapped to ATLAS, giving your security team a common language for AI threat communication.
Regulation-Aware Practice
AI regulation is arriving fast — the EU AI Act is in force, India's Digital India Act includes AI governance provisions, and SEBI and RBI are both issuing AI-specific guidance for regulated industries. ZecurX AI security engagements are structured to produce evidence of compliance with emerging AI governance requirements.
Six Specialised AI Security Capabilities
From model red-teaming to real-time output monitoring — one integrated AI security practice.
LLM Penetration Testing
Adversarial testing of large language models and GenAI applications — prompt injection, jailbreak campaigns, model extraction, and output manipulation, executed by AI security researchers. Full OWASP LLM Top 10 coverage with MITRE ATLAS technique attribution and CVSS-equivalent severity scoring.
Data Poisoning Defense
Training pipeline integrity validation, RAG data source auditing, and embedding attack detection. Covers vector database security for ChromaDB, Pinecone, Weaviate, Qdrant, and pgvector — protecting the data that shapes how your AI thinks.
AI Agent Security Audit
Autonomous agent threat modelling, tool-use sandboxing, privilege escalation testing, and human-in-the-loop bypass assessment. Full attack surface mapping for every tool, API, data source, and external system reachable by your agent.
Model Access Control
Authorisation layer design, rate limiting, abuse prevention, and anomaly detection for AI APIs. Multi-tenant isolation review, cost abuse prevention, API key management, and audit logging — the infrastructure security layer between your model and the world.
AI Threat Modelling
STRIDE and MITRE ATLAS framework application for GenAI products and intelligent systems. Attack tree construction, RAG and agentic architecture threat modelling, regulatory risk mapping, and security requirement derivation — before a line of AI code reaches production.
AI Output Monitoring
Real-time detection of abuse, policy violations, prompt injection, and behavioural drift in deployed AI model outputs. PII leakage detection, hallucination monitoring, abuse pattern analytics, and SOC integration — continuous assurance that your AI is doing what it is supposed to do.
The ZecurX AI Security Methodology
A structured approach purpose-built for the unique threat landscape of AI and LLM systems.
Architecture Review
Map AI system components, data flows, trust boundaries, and external integrations.
Threat Modelling
STRIDE + MITRE ATLAS analysis — adversarial ML technique mapping to your specific architecture.
Adversarial Testing
LLM red-teaming, prompt injection campaigns, agent exploitation, and data poisoning simulation.
Access & Control
API authorisation review, multi-tenant isolation, rate limiting, and abuse pattern testing.
Pipeline Security
Training data integrity, RAG source audit, vector DB review, and model supply chain.
Runtime Monitoring
Output policy enforcement, drift detection, and continuous behavioural assurance deployment.
What You Receive
Actionable, evidence-backed outputs — mapped to the frameworks your regulators expect.
OWASP LLM Top 10 Findings Report
CVSS-equivalent scored findings mapped to OWASP LLM Application Security Top 10 vulnerability classes, with MITRE ATLAS technique attribution, proof-of-concept evidence, and remediation guidance specific to LLM architectures — not generic security recommendations.
Executive AI Risk Dashboard
Board-ready AI risk summary with attack surface overview, severity distribution, benchmark comparison against OWASP LLM Top 10, and SLA-linked remediation timelines — for CISO reporting, investor due diligence, and AI governance review.
AI Threat Model Document
Living STRIDE + ATLAS threat model in structured format — suitable for regulatory inspection, investor due diligence, ISO 42001 certification preparation, and engineering team security reference throughout the AI product lifecycle.
Regulatory Compliance Mapping
Findings mapped to EU AI Act risk classifications, NIST AI RMF, ISO 42001, OWASP LLM Top 10, DPDPA 2023, SEBI/RBI AI guidance, and IRDAI AI circular — ready for regulators, auditors, and enterprise procurement reviews.
Proven AI security outcomes
How our AI and LLM security engagements have caught critical vulnerabilities before they became incidents — or regulatory findings.
3 Critical LLM Vulnerabilities Found Before 4.2M Policyholders Were Exposed
"ZecurX's LLM penetration test identified three critical vulnerabilities: a system prompt extraction attack that revealed the chatbot's internal configuration including the claims decision logic, an indirect prompt injection via policy document uploads that caused the model to provide incorrect claim settlement guidance, and a jailbreak path that enabled the chatbot to provide specific medical and legal advice in violation of IRDAI regulations. All three were remediated before the issues were discovered externally."
Poisoned Legal Research Assistant — Incorrect Citations Undetectable to Attorneys
"A simulated adversarial test demonstrated that a planted document containing subtly incorrect legal precedent citations caused the RAG system to incorporate the incorrect citations in subsequent legal research queries with a confidence that would have been indistinguishable from accurate results. The firm immediately implemented document ingestion controls, mandatory human review for new sources, and embedding-layer anomaly detection."
AI Agent Bypassed ₹10,000 Refund Limit via Indirect Prompt Injection
"A crafted customer complaint containing embedded instructions caused the agent to initiate a refund for an amount exceeding the ₹10,000 limit by misclassifying the transaction type, bypassing the limit check. A second finding demonstrated that the agent could be caused to reveal the full account details of any customer if the requesting customer's complaint included a specific social engineering phrase that triggered a context confusion in the model."
Behavioural Drift Detected in 4 Hours — Unlicensed Investment Advice Averted
"Six weeks after launch, ZecurX's AI Output Monitoring system detected a statistically significant increase in outputs containing specific phrases associated with direct investment advice — a regulated activity the platform was not licensed to provide through AI. Investigation revealed that a change in the system prompt introduced during a product iteration had inadvertently removed a key constraint on the model's advisory scope. Without the monitoring system, the regulatory exposure would have compounded for weeks before discovery."
Native Expertise Across the AI Ecosystem
Deep testing capability across every major LLM provider, AI framework, and deployment pattern.
LLM Providers & Models
- ◉OpenAI GPT-4o, GPT-4 Turbo, GPT-3.5
- ◉Anthropic Claude 3 (Opus, Sonnet, Haiku)
- ◉Google Gemini Pro, Gemini Ultra, PaLM 2
- ◉Meta LLaMA 2 / LLaMA 3 (self-hosted)
- ◉Mistral, Mixtral, and open-source fine-tuned variants
- ◉Azure OpenAI Service and AWS Bedrock
AI Frameworks & Infrastructure
- ◉LangChain and LlamaIndex (RAG/agent frameworks)
- ◉AutoGPT, CrewAI, and AutoGen (agent frameworks)
- ◉Hugging Face Transformers and Inference API
- ◉ChromaDB, Pinecone, Weaviate, Qdrant, pgvector
- ◉MLflow, Weights & Biases (MLOps platforms)
- ◉Vertex AI, Azure AI Studio, AWS SageMaker
Deployment Patterns
- ◉Customer-facing chatbot and virtual assistant deployments
- ◉Internal RAG knowledge base and document Q&A systems
- ◉AI coding assistants (GitHub Copilot, Cursor, CodeWhisperer)
- ◉Multi-agent pipeline and workflow automation systems
- ◉AI-powered API products and SaaS features
- ◉Embedded AI in mobile applications and IoT devices
Regulatory Alignment
Every AI security engagement aligned to the frameworks your regulators and auditors require.
Technical AI Security Standards
- ◉OWASP LLM Application Security Top 10 (2025 edition) — complete vulnerability class coverage
- ◉MITRE ATLAS (Adversarial Threat Landscape for AI Systems) — full technique matrix alignment
- ◉NIST AI Risk Management Framework (AI RMF 1.0) — Govern, Map, Measure, Manage functions
- ◉ISO/IEC 42001:2023 — AI Management System standard (certification-aligned engagements)
- ◉NIST SP 800-218A — Secure Software Development Practices for AI and ML
- ◉Google SAIF (Secure AI Framework) — six core elements mapped to ZecurX service coverage
Regulatory & Governance Frameworks
- ◉EU Artificial Intelligence Act (AIA) — risk classification, conformity assessment, and high-risk AI system requirements
- ◉India Digital India Act (DIA) — AI governance provisions and digital intermediary obligations
- ◉SEBI Circular on AI/ML — algorithmic trading and robo-advisory security requirements
- ◉RBI Guidelines on AI/ML in Financial Services — model risk management and explainability
- ◉DPDPA 2023 — AI system personal data processing, automated decision-making provisions
- ◉IRDAI Circular on Use of AI — insurance sector AI deployment approval and audit requirements
Structured for AI Deployment Velocity
From pre-launch red-teaming to continuous production monitoring — matched to your AI maturity.
Pre-Launch AI Red Team
Intensive adversarial testing engagement conducted before an AI system goes live — covering LLM penetration testing, agent security audit, access control review, and data poisoning assessment. Fixed scope, fixed timeline. Deliverable: comprehensive findings report with OWASP LLM Top 10 and MITRE ATLAS mapping. Typical duration: 3–6 weeks.
AI Threat Model & Architecture Review
Design-phase security engagement — ZecurX reviews your AI system architecture before development begins, produces a formal STRIDE + ATLAS threat model, and derives security requirements for the engineering team. Most cost-effective intervention point. Deliverable: living threat model document and security requirements specification. Typical duration: 2–3 weeks.
Continuous AI Monitoring
Ongoing production AI monitoring — ZecurX deploys and operates the output monitoring pipeline, drift detection, and abuse analytics for your AI system. Monthly reporting on detected threats, policy violations, and behavioural trends. Priced per model deployment. Ideal for regulated industries and high-volume customer-facing AI products.
AI Security Retainer
Dedicated AI security partner — quarterly red team exercises, architecture review for new AI features, continuous monitoring operation, and on-demand threat modelling. For AI-first businesses and enterprises with multiple AI deployments. Includes regulatory advisory as AI governance requirements evolve. Most comprehensive option.
Your AI is already deployed. Is it already secured?
Request a complimentary AI Security Exposure Assessment — a 45-minute consultation with a ZecurX AI security researcher who will map your AI attack surface, identify the highest-risk vulnerabilities in your current deployment, and outline what adversarial testing would reveal. Zero cost. Zero obligation. Complete clarity.