AI Security Testing: Protect Your LLMs, Agents, and AI Pipelines from Real-WorldAttacks
Your AI systems face threats that traditional security testing cannot detect. Our engineers combine deep offensive security expertise with hands-on AI systems knowledge to find prompt injection, RAG poisoning, agent hijacking, and model supply chain vulnerabilities before attackers do.
Trusted by India's leading enterprises
AI Threat Modeling and Scoping
We map your AI architecture: LLM integrations, agentic workflows, RAG pipelines, tool calls, plugin dependencies, training data sources, and model supply chain. This defines the attack surface specific to your AI implementation.
Offensive AI Security Testing
Our engineers execute targeted attacks against your AI systems: prompt injection chains, RAG poisoning, agent privilege escalation, tool call hijacking, model extraction, and data exfiltration through AI-specific attack vectors. Every test is manual and context-aware.
Validated Findings and Remediation Guidance
Each vulnerability is documented with reproducible proof-of-concept attack chains, business impact analysis, and specific remediation steps for your AI stack. Reports are mapped to ISO 42001, OWASP LLM Top 10, and applicable regulatory frameworks.
What Is AI Security Testing?
AI security testing is a specialized security assessment that identifies vulnerabilities unique to AI-powered systems, including large language models, agentic pipelines, retrieval-augmented generation architectures, and AI-integrated applications. It goes beyond traditional application security to evaluate prompt injection, model manipulation, data poisoning, and AI supply chain risks that conventional penetration testing methods cannot detect.
What We Test: AI Attack SurfaceCoverage
Comprehensive security testing across every layer of your AI implementation, from model inputs to agentic tool chains
Prompt Injection and Jailbreaking
Direct and indirect prompt injection attacks that manipulate LLM behavior, bypass safety guardrails, and extract system prompts or confidential instructions.
Agentic Pipeline Exploitation
Multi-step attacks against AI agents that hijack tool calls, escalate agent privileges, chain actions across workflows, and abuse autonomous decision-making capabilities.
RAG Poisoning and Data Manipulation
Attacks that inject malicious content into retrieval-augmented generation knowledge bases, corrupting LLM outputs and enabling indirect prompt injection through trusted data sources.
Tool Call and Plugin Hijacking
Testing whether prompt injection or crafted inputs can force AI agents to invoke unintended tools, execute unauthorized API calls, or access systems beyond their intended scope.
AI Supply Chain Risk Assessment
Evaluation of third-party model dependencies, fine-tuning data integrity, plugin security, embedding model risks, and vulnerabilities in the AI component supply chain.
Agent Privilege Escalation
Testing whether AI agents can be manipulated to exceed their intended permissions, access restricted data, perform administrative actions, or pivot across system boundaries.
Model Data Exfiltration
Attempts to extract training data, proprietary knowledge, PII, or confidential business information from LLMs through adversarial prompting techniques and output analysis.
Output Integrity and Hallucination Exploitation
Testing whether adversaries can manipulate model outputs to produce harmful, misleading, or legally problematic content that could damage your brand or mislead users.
AI Application Integration Security
Security of the integration layer between AI components and your existing application stack, including API gateways, authentication flows, data serialization, and session management around AI features.
Methodology
8 steps. Zero guesswork.
Every engagement follows this process through Lemon, our proprietary audit management platform.
AI Architecture Discovery and Threat Modeling
We begin by mapping your complete AI architecture: LLM providers, model versions, system prompts, agentic workflows, tool integrations, RAG data sources, embedding pipelines, fine-tuning datasets, and plugin dependencies. We identify trust boundaries, data flow paths, and privilege levels across all AI components. This produces a comprehensive AI threat model that guides all subsequent testing.
Prompt Injection and Input Manipulation Testing
We execute systematic prompt injection campaigns including direct injection, indirect injection through data sources, jailbreak techniques, system prompt extraction, instruction override, and context window manipulation. Testing covers single-turn and multi-turn attack scenarios, evaluating how well your safety guardrails, input filters, and prompt hardening hold up against real adversarial techniques.
Agentic Pipeline and Tool Call Exploitation
For applications with AI agents, we test the full agentic execution chain. This includes attempts to hijack tool calls through prompt manipulation, escalate agent permissions, chain tool invocations to achieve unintended outcomes, access restricted APIs through the agent, and break out of sandboxed execution environments. We simulate multi-step attack scenarios that mirror how sophisticated attackers would target autonomous AI workflows.
RAG Pipeline and Knowledge Base Security
We assess the security of your retrieval-augmented generation pipeline by testing for knowledge base poisoning, injection through ingested documents, manipulation of retrieval relevance, and exploitation of trust in retrieved context. We evaluate whether adversaries can influence AI outputs by corrupting or manipulating the data sources your LLM relies on for its responses.
AI Supply Chain and Model Risk Evaluation
We audit your AI supply chain: third-party model dependencies, fine-tuning data provenance, plugin and extension security, embedding model integrity, and configuration security of AI infrastructure. This identifies risks introduced by components outside your direct control, from model marketplaces and API providers to open-source libraries and data pipelines.
Data Exfiltration and Output Integrity Testing
We test whether adversaries can extract sensitive training data, PII, proprietary business information, or system configurations from your AI models through adversarial prompting. We also evaluate output manipulation risks including harmful content generation, hallucination exploitation, and brand safety violations that could have legal or reputational consequences.
Multi-Layer Review and Compliance Mapping
All findings undergo our structured L1/L2/L3 review process. L1 auditors document findings with full proof-of-concept attack chains. L2 senior consultants validate attack feasibility, assess coverage completeness, and identify additional test scenarios. L3 security architects perform final validation, confirm business impact assessments, and ensure findings are mapped to relevant frameworks including OWASP LLM Top 10, ISO 42001, DPDP Act, and SEBI AI governance requirements.
Reporting, Remediation Guidance, and Retesting
We deliver comprehensive reports for both technical teams and executive leadership, including reproducible attack chain documentation, AI-specific remediation guidance covering prompt hardening, guardrail implementation, privilege scoping, and architecture-level controls. Multiple rounds of retesting are included so your team can validate fixes as they are implemented. Remediation walkthrough sessions ensure your AI and development teams fully understand each finding.
"Security Brigade's structured approach through Lemon gave us complete visibility into the testing process. The three-layer review caught issues that our previous vendor missed entirely. Their reports were the first our developers could actually act on without a follow-up call."
The Platform
Powered by Lemon
Most firms rely on individual tester skill. We built a platform that makes quality structural — informed by 6,700+ previous assessments.
Structured AI Testing Workflows
Lemon defines AI-specific testing tasks, subtasks, and artifact requirements based on your architecture, ensuring complete and repeatable coverage of every AI component.
AI-Augmented Coverage Validation
AI models cross-reference testing artifacts to identify untested AI endpoints, tool integrations, or data pipeline components that auditors may have missed.
Real-Time Client Dashboard
Track findings as they are identified, monitor engagement progress, review proof-of-concept attack chains, and coordinate remediation with your team in real time.
Compliance-Ready
Audit-ready reporting for every framework
As a CERT-In empanelled firm, our reports are accepted by all major Indian and global regulators.
Industries
700+ clients across verticals
Every type of application architecture and business logic pattern — tested.
Deliverables
What you get
Reports for two audiences — executives who need the risk picture, and developers who need to fix the issues. With code-level guidance, not vague advice.
AI Threat Model and Architecture Map
Complete documentation of your AI attack surface including LLM integrations, agentic workflows, tool chains, RAG pipelines, data flows, and trust boundaries.
Technical AI Security Report
Detailed vulnerability findings with full proof-of-concept attack chains, including exact prompts, payloads, multi-step exploitation sequences, and annotated screenshots demonstrating each vulnerability.
Executive Risk Summary
Board-ready summary of AI security posture, critical risk areas, business impact analysis, and strategic remediation priorities for leadership and governance teams.
AI-Specific Remediation Guidance
Actionable remediation steps covering prompt hardening, guardrail implementation, agent privilege scoping, RAG pipeline controls, and architectural improvements tailored to your AI stack.
Compliance Mapping Report
Findings mapped to ISO 42001, OWASP LLM Top 10, DPDP Act, and applicable sector-specific AI governance frameworks for audit and compliance documentation.
Retesting and Remediation Validation
Multiple rounds of retesting included to verify that your AI engineering team has successfully resolved identified vulnerabilities. Remediation walkthrough sessions available.
What is the difference between AI security testing and traditional penetration testing?
What is prompt injection and why is it dangerous?
How long does an AI security assessment take?
Do you test agentic AI systems and multi-agent architectures?
What is RAG poisoning and how do you test for it?
Can AI security testing help with ISO 42001 compliance?
What AI supply chain risks do you assess?
How do you ensure your AI security testers understand our specific AI architecture?
Do you provide remediation support after the AI security assessment?
Is AI security testing relevant for organizations that only use third-party AI APIs?
Stay protected between assessments with ShadowMap
Continuous attack surface monitoring — discovers new assets, detects credential leaks, and alerts on new exposures the day they appear.
Secure Your AI Systems Before Attackers Find the Gaps
Book a scoping call with our AI security team. We will map your AI architecture, identify your highest-risk attack surfaces, and define a testing approach tailored to your implementation.
Typically responds within 1 business day · No commitment required