Security audit for AI & LLMs
Language models are entering your business processes. They also shift your attack surface. We audit your AI and LLM systems in practical terms using a pentest, beyond a simple theoretical review.
Why are LLMs reshaping technical audits?
An LLM is not like a “simple” web service. It combines a statistical model, opaque training data, complex prompts, business connectors and sometimes agents capable of executing actions. The attack surface therefore covers the front end, APIs, orchestration engine, the model itself and the MLOps pipelines that feed it.
The penetration test can no longer stop at the interface: it must test prompt logic, knowledge sources, tools and automated workflows. Traditional risks (XSS, injections, exfiltration) combine with scenarios specific to generative AI: poisoning, model theft and adversarial attacks. Our application security approach now integrates this probabilistic dimension.
The main families of LLM risks
Prompt injection & jailbreak
Bypassing security instructions, diverting system prompts, and manipulating inputs to make the model produce unintended behavior.
Data leakage
Exfiltration of training data, secrets, system prompts or internal rules through cleverly crafted requests.
Data poisoning
Injection of malicious data into training datasets or document bases (RAG), undermining the model’s reliability and integrity.
Agents & connectors
Forcing unauthorized calls to connected tools, plugins, ERPs or CRMs; lack of validation for actions triggered by model outputs.
MLOps pipelines
Insufficiently segregated access to weights, system prompts and policies; lack of logging and anti-extraction mechanisms.
Hallucination & integrity
Incorrect answers in critical use cases (legal, compliance, finance), requiring clear rules on when human validation remains essential.
Which reference frameworks?
The arrival of LLMs requires existing standards to be reinterpreted. TheOWASP Top 10 for LLM Applications, the NIST AI RMF and the recommendations ofANSSI provide a useful working base, but are not enough to cover all risks linked to orchestration, prompts and MLOps. Added to this is theAI Act in Europe, which now regulates AI uses. An audit effective audit combines compliance analysis, grey-box testing and adversarial attack simulations. To structure this governance sustainably, we also guide you toward the ISO 42001 standard dedicated to the management of artificial intelligence.
Our methodology
Before any testing, we carry out a precise mapping of the application and its ecosystem: data and prompt flows, RAG mechanisms, connectors and tools, MLOps pipeline. This mapping defines the scope, the components to target and the datasets to anonymize. Our approach is deliberately pragmatic: no unnecessary theory, realistic attack scenarios and directly actionable output. This audit naturally fits into a broader approach tosecurity audits and cybersecurity assessment.
The stages of an AI & LLM security audit
Mapping
Inventory of flows, prompts, RAG sources, connectors, agents and MLOps pipelines to define the audit scope.
Model & prompt testing
Prompt injection, jailbreak, exfiltration attempts, response integrity testing and hallucination assessment.
Integration & tool testing
Analysis of output consumption (HTML/JS injections), forced calls, and access controls for plugins and connectors.
MLOps & governance testing
Review of training sources, protection of weights and artifacts, logging and anti-extraction mechanisms.
Remediation plan
Prioritized debrief: securing flows, hardening integrations, supervision and review of system prompts.
Continuous loop
Shift from a one-off audit to a recurring program, with CI/CD and MLOps controls integrated to track drift.
Why choose BCIT to audit your AI systems?
Real offensive approach
We test your LLMs like an attacker, not like a checklist. Concrete scenarios, not copied theory.
From technical testing to governance
Pentest AI, compliance AI Act and ISO 42001, led by an external CISO : an end-to-end view.
Skills development
We train your teams through cybersecurity awareness to embed the right reflexes.
Are your AI systems really under control?
Let’s take 15 minutes to review your LLM use cases, your exposure and the right audit scope. We will propose realistic support adapted to your context.
Security audit for AI & LLMs
Language models are entering your business processes. They also shift your attack surface. We audit your AI and LLM systems in practical terms using a pentest, beyond a simple theoretical review.
Why are LLMs reshaping technical audits?
An LLM is not like a “simple” web service. It combines a statistical model, opaque training data, complex prompts, business connectors and sometimes agents capable of executing actions. The attack surface therefore covers the front end, APIs, orchestration engine, the model itself and the MLOps pipelines that feed it.
The penetration test can no longer stop at the interface: it must test prompt logic, knowledge sources, tools and automated workflows. Traditional risks (XSS, injections, exfiltration) combine with scenarios specific to generative AI: poisoning, model theft and adversarial attacks. Our application security approach now integrates this probabilistic dimension.
The main families of LLM risks
Prompt injection & jailbreak
Bypassing security instructions, diverting system prompts, and manipulating inputs to make the model produce unintended behavior.
Data leakage
Exfiltration of training data, secrets, system prompts or internal rules through cleverly crafted requests.
Data poisoning
Injection of malicious data into training datasets or document bases (RAG), undermining the model’s reliability and integrity.
Agents & connectors
Forcing unauthorized calls to connected tools, plugins, ERPs or CRMs; lack of validation for actions triggered by model outputs.
MLOps pipelines
Insufficiently segregated access to weights, system prompts and policies; lack of logging and anti-extraction mechanisms.
Hallucination & integrity
Incorrect answers in critical use cases (legal, compliance, finance), requiring clear rules on when human validation remains essential.
Which reference frameworks?
The arrival of LLMs requires existing standards to be reinterpreted. TheOWASP Top 10 for LLM Applications, the NIST AI RMF and the recommendations ofANSSI provide a useful working base, but are not enough to cover all risks linked to orchestration, prompts and MLOps. Added to this is theAI Act in Europe, which now regulates AI uses. An audit effective audit combines compliance analysis, grey-box testing and adversarial attack simulations. To structure this governance sustainably, we also guide you toward the ISO 42001 standard dedicated to the management of artificial intelligence.
Our methodology
Before any testing, we carry out a precise mapping of the application and its ecosystem: data and prompt flows, RAG mechanisms, connectors and tools, MLOps pipeline. This mapping defines the scope, the components to target and the datasets to anonymize. Our approach is deliberately pragmatic: no unnecessary theory, realistic attack scenarios and directly actionable output. This audit naturally fits into a broader approach tosecurity audits and cybersecurity assessment.
The stages of an AI & LLM security audit
Mapping
Inventory of flows, prompts, RAG sources, connectors, agents and MLOps pipelines to define the audit scope.
Model & prompt testing
Prompt injection, jailbreak, exfiltration attempts, response integrity testing and hallucination assessment.
Integration & tool testing
Analysis of output consumption (HTML/JS injections), forced calls, and access controls for plugins and connectors.
MLOps & governance testing
Review of training sources, protection of weights and artifacts, logging and anti-extraction mechanisms.
Remediation plan
Prioritized debrief: securing flows, hardening integrations, supervision and review of system prompts.
Continuous loop
Shift from a one-off audit to a recurring program, with CI/CD and MLOps controls integrated to track drift.
Why choose BCIT to audit your AI systems?
Real offensive approach
We test your LLMs like an attacker, not like a checklist. Concrete scenarios, not copied theory.
From technical testing to governance
Pentest AI, compliance AI Act and ISO 42001, led by an external CISO : an end-to-end view.
Skills development
We train your teams through cybersecurity awareness to embed the right reflexes.
Are your AI systems really under control?
Let’s take 15 minutes to review your LLM use cases, your exposure and the right audit scope. We will propose realistic support adapted to your context.