Real World Security Testing for Artificial Intelligence Systems, Large Language Models, and AI Powered Applications
Orasec delivers results driven AI and LLM security testing services, identifying vulnerabilities that expose AI systems, compromise large language model implementations, and undermine the security of AI powered applications and supporting infrastructure. We go beyond surface level assessments by combining certified security testers, advanced adversarial methodologies, and real world attack simulation to uncover security weaknesses that genuinely impact organisations building and deploying artificial intelligence and large language model technology.
AI and LLM deployments are emerging as high value attack targets. Prompt injection vulnerabilities, training data exposure, model manipulation, insecure plugin architectures, and unsafe output handling create an entirely new attack surface that demands security testing built specifically for AI systems not generic application security assessments repurposed for machine learning environments.
Organisations deploying large language models and AI powered applications introduce new attack vectors that traditional security testing does not address. Attackers target AI systems to manipulate model behaviour, extract sensitive training data, bypass safety controls, abuse integrated tools and plugins, and use AI systems as pivot points into connected infrastructure and data environments.
Orasec's AI and LLM security testing methodology tests every layer of your AI environment from model inputs and prompt handling to plugin integrations, retrieval augmented generation pipelines, API security, and supporting infrastructure ensuring your AI security posture is resilient against the real world threats targeting artificial intelligence deployments today.
Prompt injection is the most prevalent and impactful vulnerability class in large language model deployments. Direct prompt injection manipulates model behaviour through crafted user inputs. Indirect prompt injection embeds malicious instructions in external content documents, web pages, database records that the model retrieves and processes, causing it to execute attacker controlled instructions without user awareness.
AI models trained on sensitive organisational data create data exposure risks through model inversion attacks, membership inference, and training data extraction techniques. Attackers can systematically query models to reconstruct sensitive training data, extract proprietary information, and identify individuals whose data was used in model training.
LLM applications integrate with external tools, APIs, databases, and services through plugin architectures. Insecure plugin implementations, excessive tool permissions, and inadequate input validation create paths for prompt injection to trigger unauthorised tool execution turning a language model into an attack vector against connected systems.
RAG architectures retrieve external documents and data to augment model responses. Poisoned retrieval sources, insecure document handling, and inadequate content filtering create indirect prompt injection paths and data exposure risks across RAG pipeline implementations.
Computationally expensive prompt crafting, recursive processing loops, and resource exhaustion techniques create denial of service conditions against AI systems degrading model availability, inflating inference costs, and disrupting AI dependent business operations.
LLM outputs consumed by downstream systems without adequate validation create injection vulnerabilities in connected applications. SQL injection, command injection, cross site scripting, and server side request forgery payloads delivered through model outputs exploit downstream systems that trust and process LLM generated content without sanitisation.
AI systems depend on third party models, datasets, libraries, and infrastructure. Compromised model weights, poisoned training datasets, malicious fine tuning, and insecure model supply chains create integrity risks that undermine the security and behaviour of deployed AI systems.
We conduct comprehensive prompt injection testing against LLM deployments covering direct injection through user interfaces, indirect injection through retrieved content sources, jailbreaking techniques, system prompt extraction, and role confusion attacks. Testing confirms whether prompt injection can manipulate model behaviour, bypass safety controls, and trigger unauthorised actions across AI system implementations.
Our testers conduct full stack security testing of LLM powered applications including web and API interfaces, authentication controls, session management, input handling, output sanitisation, and backend infrastructure. Testing identifies vulnerabilities allowing account takeover, data exposure, and unauthorised system access through AI application attack surfaces.
We assess LLM plugin architectures and tool integrations for insecure implementations, excessive permissions, inadequate input validation, and prompt injection paths that allow attackers to trigger unauthorised tool execution including file system access, API calls, database queries, and code execution through connected plugin systems.
Our testing evaluates retrieval augmented generation pipeline implementations for poisoning vulnerabilities, insecure document handling, content filtering weaknesses, and indirect prompt injection paths across document retrieval, processing, and model augmentation workflows.
We assess AI systems for training data extraction vulnerabilities, membership inference risks, model inversion attack exposure, and proprietary information leakage through systematic model querying and adversarial input techniques.
Our testing evaluates the infrastructure supporting AI deployments including model serving APIs, inference endpoints, model registries, training pipelines, and cloud infrastructure for misconfigurations, access control weaknesses, and vulnerabilities that create paths to model compromise and data exposure.
Model architecture, input handling, output processing, plugin integrations, retrieval sources, API interfaces, and supporting infrastructure are mapped to establish a complete picture of exploitable entry points across the AI environment.
Systematic prompt injection campaigns are conducted across direct and indirect attack surfaces testing model responses to crafted inputs, retrieved content manipulation, jailbreaking attempts, and instruction hierarchy attacks across all model interaction points.
Plugin architectures, tool integrations, and external service connections are assessed for insecure implementations, excessive permissions, and prompt injection paths that allow attacker controlled model outputs to trigger unauthorised actions in connected systems.
Systematic model querying, membership inference techniques, and training data extraction methods are applied to assess the risk of sensitive data exposure through model outputs and adversarial probing.
LLM application interfaces, APIs, authentication systems, and supporting infrastructure are assessed using standard penetration testing techniques adapted for AI connected application environments.
Findings are delivered in a detailed report with risk ranked vulnerabilities, exploitation evidence, attack path documentation, and prioritised remediation guidance tailored to AI development and deployment constraints.
High level risk overview communicating AI security posture, key findings, and business impact for leadership and technical stakeholders
Detailed vulnerability documentation with exploitation evidence, attack paths, and risk ratings across all identified AI and LLM security weaknesses
Dedicated findings covering direct and indirect prompt injection vulnerabilities, jailbreaking exposure, and system prompt extraction risks across tested AI systems
Comprehensive findings covering plugin architecture vulnerabilities, excessive tool permissions, and unauthorised action execution paths
Findings covering training data extraction risks, membership inference exposure, and proprietary information leakage through model outputs
Risk ranked recommendations with practical guidance tailored to AI development workflows, model deployment constraints, and LLM application architecture
Validation testing confirming remediation effectiveness across critical AI and LLM security findings
Our testers bring deep expertise in adversarial machine learning, prompt injection techniques, LLM application security, and AI infrastructure assessment specialised knowledge that general penetration testers do not possess.
Our AI and LLM security testing methodology is purpose built for artificial intelligence attack surfaces covering prompt injection, plugin abuse, RAG pipeline security, and model extraction techniques that no traditional penetration testing framework addresses.
AI security testing requires creative, manual adversarial thinking. Our testers craft targeted prompt injection campaigns, systematic data extraction probes, and plugin abuse scenarios that automated scanning tools cannot replicate.
The AI threat landscape evolves faster than any other security domain. Orasec maintains current expertise across emerging LLM attack techniques, new prompt injection vectors, and evolving AI security research to ensure assessments reflect the latest real world threats.
From model inputs and prompt handling to plugin integrations, RAG pipelines, application interfaces, APIs, and cloud infrastructure, Orasec provides complete AI and LLM security testing coverage across your entire artificial intelligence environment.
Every finding is documented with exploitation evidence, real world impact context, and remediation guidance that AI development and security teams can act on within existing model development and deployment workflows.
Connect with Orasec's certified testers to assess your large language model deployment, AI powered application, plugin architecture, RAG pipeline, or AI infrastructure. Identify real vulnerabilities before attackers exploit them.