AI & LLM Security
Assessment

Go beyond traditional VAPT. We secure your entire AI ecosystem - from prompt injection testing to data poisoning defense and adversarial robustness audits.

Why AI Security Is No Longer Optional

AI adoption is exploding - and so are AI-specific attack surfaces. Every LLM deployment, every RAG pipeline, every AI agent introduces risks that traditional security testing completely misses.

Prompt Injection & Jailbreaking

Adversaries bypass AI system safety guards through malicious prompts, forcing LLMs to leak internal data, execute unauthorized code, or generate harmful content.

Training Data Poisoning

Attackers manipulate the model's behavior by injecting malicious data into the training pipeline or RAG vector stores, creating backdoors for future exploitation.

Insecure Output Handling

AI-generated outputs are often implicitly trusted. Without proper validation, LLM outputs can trigger XSS, SQLi, or RCE when processed by downstream applications.

Deep AI Security Expertise

Briskinfosec provides end-to-end security for AI developers and enterprise AI users.

LLM & RAG Security

We stress-test your Large Language Model applications and Retrieval-Augmented Generation stacks against the OWASP LLM Top 10.

▸ Indirect prompt injection via data sources
▸ Vector database sensitive data leakage
▸ Prompt leaking and IP extraction
▸ System prompt bypass & guardrail testing
▸ Automated red-teaming for LLM reliability

AI Agent & Agentic Security

Autonomous AI agents with tool access (code execution, web browsing, API calls) multiply risk exponentially. We test agent architectures for safety.

▸ Tool-use exploitation & privilege escalation
▸ Multi-agent coordination attack vectors
▸ Sandbox escape & execution boundary testing
▸ Memory manipulation in persistent agents
▸ Goal hijacking & reward hacking

AI Governance & Compliance

Navigate the evolving regulatory landscape for AI systems - EU AI Act, NIST AI RMF, ISO 42001, and industry-specific requirements.

▸ EU AI Act risk classification & compliance
▸ NIST AI RMF alignment assessment
▸ ISO 42001 AI Management System readiness
▸ AI bias & fairness testing for regulated sectors
▸ AI transparency & explainability audit

Our AI Security Assessment Process

A structured, repeatable methodology that maps every AI component, tests every attack surface, and delivers actionable remediation guidance.

AI Asset Discovery

Map all AI/ML components: models, APIs, pipelines, vector stores, training data sources, plugins, and Shadow AI instances across the organization.

Threat Modeling

STRIDE-based threat modeling specifically for AI systems. Map attack surfaces per OWASP LLM Top 10, MITRE ATLAS, and NIST AI framework.

Adversarial Testing

Active exploitation: prompt injection, jailbreaking, data extraction, model inversion, adversarial inputs, and abuse scenario testing against your live AI systems.

Infrastructure Review

Assess AI infrastructure: API security, authentication, rate limiting, data encryption, model serving infrastructure, MLOps pipeline security.

Governance Audit

Evaluate AI governance policies, responsible AI practices, model monitoring, incident response, and compliance with EU AI Act / NIST AI RMF.

Report & Remediate

Executive summary + technical deep-dive with CVSS-scored findings, PoC demonstrations, risk-prioritized remediation roadmap, and re-validation support.

Why Briskinfosec for AI Security

CREST

India's Only CREST-Approved for Both VA & PT

International gold standard in security testing, applied to cutting-edge AI assessment methodologies.

AI-First

Dedicated AI Security Research Team

Not security generalists doing AI - our AI security team researches novel attack vectors and publishes findings on LLM and agent vulnerabilities.

540+

Organizations Trust Our Expertise

From fintech AI chatbots to healthcare ML pipelines, we've assessed AI systems across every major industry vertical.

Common AI Security Vulnerabilities

What is Prompt Injection?

Prompt Injection is an attack where an adversary provides specially crafted input to an LLM that causes it to ignore its original instructions and perform unintended actions, like leaking system prompts or sensitive data.

How do you test AI agents?

We use adversarial testing to see if an agent can be manipulated into misusing its granted tools (like APIs or terminal access) to perform unauthorized operations or escalate its privileges within your infrastructure.

Does this audit cover bias and fairness?

Yes, our governance review includes assessing your AI systems for unintended algorithmic bias and ensuring compliance with emerging "Responsible AI" regulations like the EU AI Act.