AI Security (LLM, Agent & RAG)
Secure your AI systems against emerging threats. From LLM red teaming to agent security to RAG pipeline protection - we make your AI safe for production.
Services
AI Security Services
LLM Red Teaming
Adversarial testing of LLM applications - prompt injection, jailbreaking, data extraction, and output manipulation to identify vulnerabilities before attackers do.
RAG Security
Secure your retrieval-augmented generation pipelines - access control, data poisoning prevention, context window attacks, and information leakage protection.
Agent Security
Security assessment of AI agent systems - tool abuse, privilege escalation, autonomous action boundaries, and human-in-the-loop enforcement.
Data Privacy & PII Protection
Prevent PII leakage in AI systems - input/output filtering, data anonymization, differential privacy, and compliance with GDPR/CCPA.
Model Security Assessment
Evaluate model security - supply chain risks, model poisoning, backdoor detection, and secure model deployment practices.
AI Governance Framework
Develop AI governance policies - responsible AI guidelines, bias monitoring, transparency requirements, and regulatory compliance (EU AI Act, NIST AI RMF).
What We Do
What AI Security Assessment Involves in Practice
AI security is a distinct engineering discipline from traditional application security, and it requires practitioners who understand both the attack surface and the underlying architecture of the systems being assessed. LLM applications, RAG pipelines, and AI agent systems each introduce vulnerability classes that do not exist in conventional software: prompt injection that manipulates model behavior, retrieval poisoning that corrupts knowledge bases, and agent tool abuse that causes autonomous systems to take actions outside their intended boundaries. These cannot be found by running a standard web application scanner against an AI endpoint.
LLM red teaming is the structured adversarial testing of language model applications. We systematically attempt prompt injection, jailbreaking, data extraction, and output manipulation using techniques mapped to the OWASP LLM Top 10 and NIST AI RMF. Direct prompt injection targets the model through the user input channel. Indirect prompt injection targets the model through external content it retrieves or processes, including documents, web pages, and database records that an attacker can influence. Both require different testing approaches and different mitigations. We test both against your actual deployment, not a sandbox approximation of it.
RAG pipeline security covers the full retrieval stack: the data ingestion process that populates the knowledge base, the access controls that determine which documents are retrievable for which users, the chunking and embedding pipeline that processes source documents, and the context assembly that determines what the model receives before generating a response. Each stage presents a distinct attack surface. Data poisoning at ingestion, access control bypass at retrieval, and context window manipulation at assembly each require specific controls. We assess the architecture end to end rather than treating RAG security as a single concern.
Threat Landscape
AI Threats We Defend Against
Prompt Injection
Direct and indirect prompt injection attacks that manipulate LLM behavior
Data Exfiltration
Extracting training data, PII, or sensitive context from AI systems
Agent Hijacking
Manipulating AI agents to perform unauthorized actions via tool abuse
RAG Poisoning
Injecting malicious content into knowledge bases to corrupt AI outputs
Model Supply Chain
Compromised models, poisoned fine-tuning data, and malicious adapters
Output Manipulation
Steering AI outputs for misinformation, bias amplification, or harmful content
Deliverables
What You'll Receive
Governance and Compliance
AI Security Across Regulatory Frameworks and Deployment Contexts
NIST AI RMF and EU AI Act
The NIST AI Risk Management Framework structures AI risk across four functions: Govern, Map, Measure, and Manage. It provides a voluntary framework for identifying and mitigating risks introduced by AI systems across their lifecycle. The EU AI Act introduces binding requirements for high-risk AI systems, including conformity assessments, transparency obligations, and human oversight requirements. Organizations developing or deploying AI in regulated contexts need governance frameworks that address both the technical security risks and the regulatory obligations. We develop AI governance policies aligned to NIST AI RMF and structured for EU AI Act compliance where it applies, grounded in what your systems actually do rather than generic policy templates.
AI in Healthcare and PHI
Healthcare organizations deploying AI systems that process, generate, or retrieve content involving PHI face HIPAA obligations that apply regardless of whether a human or an AI system handles the information. LLM applications with access to patient records, clinical notes, or medical histories must implement access controls, audit logging, and minimum necessary access principles at the AI layer as well as the data layer. RAG pipelines retrieving from PHI data sources require access controls that prevent cross-patient information leakage through retrieval and context assembly. We assess AI deployments in healthcare environments against HIPAA technical safeguard requirements and identify gaps specific to how AI systems interact with protected health information.
AI Agent Security
Autonomous AI agents that can invoke tools, execute code, browse the internet, and interact with external services present a security surface that extends far beyond the model itself. An agent that can send email, query databases, and modify files in response to natural language instructions is a powerful attack vector if its action boundaries, authorization model, and human-in-the-loop requirements are not carefully designed. We assess agent architectures for privilege escalation paths, tool abuse scenarios, prompt injection vectors that could redirect agent behavior, and the absence of human oversight controls in decision paths that have significant real-world consequences.
FAQ
Common Questions About AI Security
What is prompt injection and why is it a critical AI security risk?
Prompt injection is an attack in which malicious input causes a language model to deviate from its intended behavior, follow attacker instructions instead of legitimate user instructions, or disclose information it should not. Direct prompt injection is delivered through the user input channel. Indirect prompt injection is delivered through external content the model processes, such as documents it retrieves, web pages it browses, or database records it reads. Indirect prompt injection is particularly dangerous because the attack is embedded in content the model trusts rather than input it might be prompted to treat skeptically. It is the most commonly exploited LLM vulnerability class in production deployments and the one most likely to be missed by organizations that test only the direct user input surface.
What is RAG security and what specific risks does a RAG pipeline introduce?
Retrieval-augmented generation pipelines retrieve documents from a knowledge base and provide them as context to a language model before it generates a response. The security risks are specific to how retrieval and context assembly work. Access control bypass at the retrieval layer can expose documents to users who should not have access to them, because retrieval is often implemented without the same access controls that govern the underlying data source. Data poisoning at the ingestion layer can corrupt the knowledge base with malicious content that influences model outputs. Context window attacks exploit the retrieval and assembly logic to cause the model to receive attacker-controlled content alongside legitimate documents. Each of these requires different testing and different mitigations.
What is the OWASP LLM Top 10 and how does it guide AI security assessments?
The OWASP Top 10 for Large Language Model Applications is a community-maintained list of the most critical security risks in LLM deployments, covering prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. It provides a structured framework for scoping AI security assessments and ensures consistent coverage of the vulnerability classes most likely to affect production LLM deployments. We map our LLM red team findings to OWASP LLM Top 10 categories so your development team has a shared reference point for understanding findings and prioritizing remediation, and so your security program can track coverage across the risk surface over time.
How does AI governance differ from AI security?
AI security addresses the technical vulnerabilities in AI systems: prompt injection, data leakage, model poisoning, agent misuse, and the infrastructure that hosts and serves AI components. AI governance addresses the organizational policies and processes that determine how AI systems are developed, deployed, monitored, and retired: risk classification, human oversight requirements, transparency obligations, bias monitoring, and regulatory compliance. The two are related but distinct. A well-secured AI system can still cause harm if its governance framework permits deployment in high-risk contexts without appropriate oversight. We develop governance frameworks that address both dimensions, with technical security controls aligned to the risk classification and oversight requirements defined in the governance layer.
What should organizations do before deploying an LLM application in production?
Before production deployment, an LLM application should be assessed for the vulnerability classes most likely to affect it given its specific architecture, data sources, and user population. This includes testing for direct and indirect prompt injection, verifying that output handling does not pass model-generated content to downstream systems without sanitization, confirming that access controls at the retrieval layer match the access controls on the underlying data source, and verifying that the model cannot be induced to disclose sensitive information from its context window or system prompt. For applications processing regulated data including PHI or PII, the assessment should also verify that input and output filtering, logging, and data retention are implemented in compliance with the applicable regulatory requirements before any production traffic is handled.
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