AI Security Testing: Protect Your LLMs, Agents, and AI Pipelines from Real-World Attacks
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
Why the old test misses
The application boundary moved, and the test boundary did not
A conventional application test assumes the system does what its code says. An AI-enabled system does what its code says plus whatever the model was persuaded to do, and those are different attack surfaces. The interesting failures are no longer only in the request path. They are in the instructions: content that reaches the model from a document, a web page, a support ticket or a database field, carrying text that the model treats as direction. They are in the tools: an agent granted the ability to call an API, read a file or send a message, where the security question is not whether the tool works but what the model can be induced to do with it. They are in the boundary between users: retrieval that pulls context the current user should never see, or a cache that serves one tenant’s answer to another. And they are in the output path, where a model response is rendered, executed or passed downstream by code that trusts it. Testing this properly means treating the model as an untrusted component inside your own system, which is uncomfortable but accurate, and then asking the ordinary security questions about everything it can reach. That is the assessment: the application, the API, the retrieval layer, the tool permissions and the data the whole thing is standing on.
What we test
Five surfaces, in the order they usually fail
-
One
Prompt injection, direct and indirect
Whether instructions reaching the model from user input or from retrieved content can change its behaviour, and what the changed behaviour can then do. Indirect injection through documents and web content is the version most systems have never been tested against.
-
Two
Tool and agent permissions
Where the model can call tools, we test what it can be induced to call and with what arguments. An agent with a broad API token is an authorisation problem wearing a new hat, and the blast radius is whatever that token can reach.
-
Three
Retrieval and tenant isolation
Whether the retrieval layer can be made to return context belonging to another user, another tenant or another classification, including through the embedding store and any cache in front of it.
-
Four
Output handling
What your code does with the model response. Rendering it, executing it, or passing it to a downstream system without treating it as untrusted input is where a model problem becomes an application compromise.
-
Five
The surrounding application
Authentication, authorisation, the API, the infrastructure and the data stores. The AI layer attracts attention and most real compromises still arrive through the ordinary surface underneath it.
Which system is it
The architecture decides the assessment
| System | Where the risk concentrates | What we prioritise |
|---|---|---|
| A chat feature on your product | Direct injection, output rendering, and the entitlement boundary between what the feature can see and what the user may see. | Output handling and per-user authorisation |
| Retrieval over your own corpus | Indirect injection through ingested content, and retrieval crossing a tenant or classification boundary. | Ingestion trust and retrieval isolation |
| An agent with tools | What the model can be induced to invoke, and what the credentials behind each tool can reach. | Tool scope, argument validation and blast radius |
| A model you host | The serving infrastructure, the weights and the supply chain that produced them, in addition to everything above. | Infrastructure and model supply chain |
A chat feature on your product
- Where the risk concentrates
- Direct injection, output rendering, and the entitlement boundary between what the feature can see and what the user may see.
- What we prioritise
- Output handling and per-user authorisation
Retrieval over your own corpus
- Where the risk concentrates
- Indirect injection through ingested content, and retrieval crossing a tenant or classification boundary.
- What we prioritise
- Ingestion trust and retrieval isolation
An agent with tools
- Where the risk concentrates
- What the model can be induced to invoke, and what the credentials behind each tool can reach.
- What we prioritise
- Tool scope, argument validation and blast radius
A model you host
- Where the risk concentrates
- The serving infrastructure, the weights and the supply chain that produced them, in addition to everything above.
- What we prioritise
- Infrastructure and model supply chain
What you receive
Findings an engineer can act on, and a position a board can read
Reproducible findings
Each one with the exact input, the observed behaviour and the fix. Model behaviour is probabilistic, so a finding is recorded with the conditions under which it reproduces and how often, and never as a single lucky screenshot.
Mapped to the OWASP LLM Top 10
Findings mapped to the OWASP Top 10 for Large Language Model Applications, and to ISO 42001 and DPDP obligations where those apply to your deployment.
Tool and permission review
A written position on what each tool and credential in the system can reach, which is frequently the first time anyone has assembled that view.
Retest on the fixes
Guardrails and prompt changes are easy to declare and hard to verify, so fixes are retested under the conditions that produced the original finding.
Methodology
What happens between kickoff and the report
Every engagement follows this process through Lemon, our proprietary audit management platform.
AI Architecture Discovery and Threat Modelling
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.
"We needed an assessment on a 3-week timeline because of a partner integration deadline. Security Brigade turned it around in 18 days, including the L2 and L3 reviews. The report was regulator-ready — we submitted it to our partner's compliance team unchanged. When speed and credibility both matter, they're the only call we make."
FAQ
AI security testing, answered
What is in scope and what it produces. Talk to our team about your architecture.
Contact usHow is this different from a normal application penetration test?
What is indirect prompt injection?
Model behaviour is probabilistic. How can a finding be reproducible?
Do you test agents that can take actions?
What frameworks do you map findings to?
Stay protected between assessments with ShadowMap
Continuous attack surface monitoring: it 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
The platform underneath
Every engagement runs on B-52.
Testing a model is not testing an application that happens to call one. The questions are what the model can be persuaded to disclose, what tools it can be made to reach, and what its context window has already absorbed.