Test your AI before attackers do
Chatbots, RAG systems and AI agents introduce attacks traditional security testing doesn't cover. We find them, fix them and retest.
The problem
An AI assistant can be talked into ignoring its instructions. A RAG system can leak documents it was never meant to show. An agent with tool access can be manipulated into taking actions on an attacker's behalf. These risks sit in the AI layer, and standard penetration tests rarely look there.
What the research says
of organizations say attacks targeting AI systems are among the threats they're least prepared for.
of security and finance leaders expect cyber budgets to increase.
Research found that Arabic written in Latin script (Arabizi) bypassed safety controls in leading AI models where standard Arabic did not.
Source: Al Ghanim et al., EMNLP 2024
What AI security testing covers
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Threat modeling
Your AI stack reviewed against the OWASP Top 10 for LLM Applications.
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Prompt injection and jailbreak testing
Direct attacks, plus indirect attacks hidden in documents, emails and web content your AI reads.
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Data leakage testing
Checks for exposure of sensitive data through RAG retrieval, system prompts and outputs.
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Agent misuse review
Excessive permissions, unsafe tool use and missing human approval steps.
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Multilingual testing
Attacks in Arabic, Arabizi, Urdu and Hindi as well as English.
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Guardrails and monitoring
Input and output filtering and alerting set up for the issues we find.
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Remediation and retest
A prioritized fix list, then a retest to confirm the fixes hold.
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Agentic AI threats
Goal hijacking, tool misuse, memory poisoning and privilege abuse, tested against the OWASP Top 10 for Agentic Applications.
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AI supply chain review
Third-party models, plugins, MCP tool servers and datasets checked for what they can reach and change.
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RAG and data poisoning
Tests for planted content in the documents and vector stores your assistant retrieves from.
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Output handling
Checks that AI output cannot inject code, links or commands into the systems that display it or act on it.
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Cost abuse and denial of wallet
Tests for prompts and agent loops that run up your bill or slow the service down.
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Continuous testing
An automated test suite you keep and rerun after every model, prompt or tool change.
Deliverables
- Threat model of your AI application
- Findings report ranked by severity, with reproduction steps
- Remediation guidance and guardrail configuration
- Retest report
This service covers the AI layer of your applications: chatbots, voice agents, RAG systems and AI agents. It complements, and does not replace, infrastructure and network penetration testing. All testing is done only with written authorization and an agreed scope.
Who it's for
- Companies running customer-facing chatbots or voice agents
- Teams with internal assistants connected to sensitive documents
- Companies giving AI agents access to tools, systems or payments
Frameworks we test against
OWASP Top 10 for LLM Applications (2025)
Prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption.
OWASP Top 10 for Agentic Applications
Risks specific to AI agents, such as goal hijacking, tool misuse, privilege abuse and memory poisoning.
MITRE ATLAS
A knowledge base of real adversary tactics and techniques against AI systems.
NIST AI 100-2
NIST's taxonomy of adversarial machine learning attacks and mitigations.
NIST AI Risk Management Framework
We test against these public frameworks. We are not affiliated with OWASP, MITRE or NIST.
How we work
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Scoping call
We learn how you use AI today and agree what matters most.
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Fixed-scope assessment
A defined assessment with a clear deliverable, so you know the cost upfront.
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Implementation
We fix what we find, alongside your team.
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Ongoing support (optional)
Monitoring, reviews and retesting as your AI use grows.
AI security testing questions
How is this different from a regular pentest?
A regular pentest targets networks, servers and web apps. We target how your AI behaves: what it can be tricked into saying, revealing or doing.
Will testing affect our live system?
We agree the environment in advance and prefer staging. If testing must touch production, we schedule it and limit it to safe techniques.
Do you test voice agents?
Yes. Voice agents face the same manipulation risks as chatbots, plus some specific to speech.
Why test in Arabic?
Most AI safety work has focused on English. Research has shown non-English and transliterated inputs can slip past safeguards, so if your users speak Arabic, so do potential attackers.
What is LLM red teaming?
Attacking your AI system the way a real adversary would, to find what it can be made to say, reveal or do before someone else does. Findings are mapped to the OWASP lists and MITRE ATLAS so they line up with known risk categories.
Do you test AI agents with tool access and MCP servers?
Yes. Tools and MCP servers are where an AI moves from saying things to doing things, so we test what an attacker could make the agent do with each tool and permission it holds.
How often should AI be retested?
After every change that alters behaviour: a new model version, new prompts, new tools or new data sources. We leave you an automated suite for exactly that.
Ready to see where you stand?
Book a free scoping call. We'll tell you honestly whether this is a fit.