Adversarial testing for LLM applications, RAG pipelines and AI agents — prompt injection, model abuse and data leakage, uncovered by certified AI/ML penetration testers before your AI reaches production.
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Filters and system prompts are not security boundaries. Indirect injection through documents, emails and web pages quietly redirects your model to serve the attacker's intent.
Retrieval systems index everything you gave them. Without access controls tested end to end, one clever query can surface data no user should ever see.
When your AI can call APIs, send emails or execute code, a hijacked agent isn't a chat problem — it's a foothold in your business systems.
The industry is shipping LLM features faster than it's securing them — and the first large-scale studies of AI applications are finding the gaps.
of security teams admit they've used AI tools without a formal security assessment in place.
Source: Gartner survey reportingPrompt injection is OWASP's #1 risk for LLM applications — and indirect injection through retrieved content is the vector defenders see least.
Source: OWASP Top 10 for LLM ApplicationsLLM-specific vulnerabilities documented by MITRE ATLAS — real attacker techniques demonstrated against deployed AI systems, not theory.
Source: MITRE ATLASHow quickly leaked chat transcripts, retrieval data and agent actions surface once a model is compromised — with no traditional perimeter to slow an attacker down.
Industry incident patternsFigures reflect published industry research and frameworks at time of writing; sources cited per card. We're happy to walk through the underlying studies on a scoping call.
LLM and AI penetration testing is a controlled adversarial security assessment in which qualified testers attack AI applications, models, retrieval systems and agents to uncover exploitable weaknesses such as prompt injection, data leakage, tool misuse and broken trust boundaries.
Tests direct and indirect prompt injection, jailbreaks and instruction-conflict scenarios designed to override intended behaviour.
Challenges RAG pipelines, retrieval permissions and model context to determine whether sensitive or cross-tenant data can be exposed.
Tests whether compromised prompts can cause agents to misuse tools, call APIs, execute actions or reach systems beyond their intended permissions.
Captures reproducible attack transcripts and proven business impact so teams can harden the model, application and surrounding controls.
AI penetration testing subjects your LLM applications, chatbots, copilots and agents to the same adversarial pressure a real attacker applies — probing prompt injection, jailbreaks, data extraction, tool misuse and the traditional web flaws hiding beneath the model layer.
Our certified AI/ML penetration testers chain model-level findings into business impact: can a customer's document make your assistant exfiltrate another tenant's data? Can a hijacked agent reach internal APIs? Can your fine-tuned model be walked past every guardrail you built?
The result is a report that shows exactly how your AI was attacked, what it revealed, and how to harden it — evidence you can show to boards, regulators, insurers and customers.
As a UK provider working under the NCSC CHECK scheme, our testing is recognised across UK government, financial services and regulated industries — and maps directly to Cyber Essentials Plus, ISO 27001, UK GDPR, DORA and NIS2 obligations faced by UK organisations deploying AI.
Benchmarks tell you how your model performs on average. Adversarial testing proves how it behaves when someone is actively trying to break it — the scenario regulators, customers and your board actually care about.
The industry is shipping LLM features faster than it's securing them — and the first large-scale studies of AI applications are finding the gaps.
of security teams admit they've used AI tools without a formal security assessment in place.
Source: Gartner survey reportingPrompt injection is OWASP's #1 risk for LLM applications — and indirect injection through retrieved content is the vector defenders see least.
Source: OWASP Top 10 for LLM ApplicationsLLM-specific vulnerabilities documented by MITRE ATLAS — real attacker techniques demonstrated against deployed AI systems, not theory.
Source: MITRE ATLASHow quickly leaked chat transcripts, retrieval data and agent actions surface once a model is compromised — with no traditional perimeter to slow an attacker down.
Industry incident patternsFigures reflect published industry research and frameworks at time of writing; sources cited per card. We're happy to walk through the underlying studies on a scoping call.
We scope around how your AI is actually used — the prompts, the data it can reach and the tools it can invoke — not a generic chatbot checklist.
The model is rarely the weak point on its own. We test the retrieval layer, the plugins, the identity boundaries and the app code around it — because that's where your AI actually breaks.
Every engagement is mapped to the OWASP LLM Top 10 and MITRE ATLAS, then pushed past them — attacker chains specific to your prompts, data and tools.
We exercise tool calls, plugin permissions and sandbox escapes against working systems — under agreed rules of engagement, so your agent's real capabilities get tested, not a demo.
Findings come with concrete fixes at the layer that owns them — prompt architecture, retrieval authorisation, output filtering — not a shrug that says "LLMs are probabilistic".
Prompt updates, model swaps and new plugins can reopen old holes. We re-run your adversarial baseline monthly, so drift gets caught before attackers find it.
Every engagement follows the same business-first process — scoped precisely to the systems that matter to your P&L.
Prompt injection, model abuse, RAG data leakage and agent tool misuse — tested before your AI goes live.
ApplicationsAuthentication flows, business logic and the full OWASP Top 10 — attacked the way a real adversary would.
ApplicationsREST and GraphQL endpoints, token handling, and authorisation gaps exposed through the interfaces your partners rely on.
NetworksExternal perimeters, internal networks and Active Directory — from internet-facing foothold to domain compromise.
ApplicationsiOS and Android apps dissected for insecure storage, weak transport and tampering risks on real devices.
NetworksCorporate Wi-Fi, rogue access points and WPA2/WPA3 weaknesses — tested on-site, at your offices.
Six phases, from scoping through to ongoing assurance. Every phase is documented, every finding is actionable.
We map your AI attack surface — the model, its prompts, its data sources, its tools and integrations — and agree safe testing boundaries with your team.
Adversarial testing simulates real-world abuse: direct and indirect prompt injection, jailbreaks, RAG data extraction, agent tool misuse and traditional exploitation of the application layer around your model.
Every finding is written as an attacker chain — the injection vector, what it reached, and the business data or action at the end of it — with the exploit transcript included, severity scored against the OWASP LLM Top 10, and remediation owned by the layer that can actually fix it. Reports land within 5 working days.
AI fixes fail in the details. We advise on prompt architecture, retrieval-level authorisation, output handling and agent permission boundaries — then work alongside your engineers, by call or on-site, until the guardrails hold.
We replay the full attack chains, not just the individual findings — because a patched injection is useless if the same prompt still routes through an unpatched tool call.
A model update, a new plugin or a system-prompt tweak can silently reopen a fixed flaw. We re-run your adversarial baseline every month for 12 months and flag any drift before it becomes exposure.
Fixed-fee, CHECK-certified, scoped around what your AI can actually reach — its prompts, its data, its tools. One call is enough to size it.
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