Enterprise AI is moving fast, but without governance, it is moving blind.
The OWASP Top 10 for Large Language Model Applications 2025 landed with a clear message: the security risks of LLM-powered systems are real, growing, and largely unaddressed. From prompt injection to unbounded consumption, from data poisoning to excessive agency, the attack surface of modern AI applications extends far beyond what traditional security tools were built to handle.
For enterprises in finance, healthcare, insurance, and other regulated industries, these are not theoretical risks. They are audit findings waiting to happen, compliance gaps waiting to be exploited, and reputational threats waiting to surface.
Contextier was built to address these threats at the platform level - not as an afterthought, but as the foundation through which all AI workflows are governed, observed, and controlled.
LLM01: Prompt Injection
Prompt injection remains the number one threat to LLM applications. Whether it is direct user manipulation or hidden instructions embedded in external documents, the consequences range from data leaks to full privilege escalation.
OWASP emphasizes there is no foolproof prevention for prompt injection - only layers of mitigation.
How Contextier addresses it:
Contextier’s guardrail engine sits between every user interaction and the underlying model. Inputs are scanned for injection patterns before reaching any provider. Output filtering checks responses against configured rules before they leave the platform. For multi-model architectures, a prompt injection that might succeed against a single endpoint gets intercepted at the orchestration layer, where policy enforcement is deterministic, not probabilistic.
LLM02: Sensitive Information Disclosure
LLMs embedded in enterprise applications risk exposing PII, financial data, health records, proprietary algorithms, and confidential business logic. System prompt restrictions alone are insufficient and can be bypassed.
How Contextier addresses it:
Contextier enforces data handling at the platform level, not the model level. PII detection and masking runs on every request and response, producing durable audit records with entity types, confidence scores, and masking actions. Role-based access controls ensure users only interact with authorized models and data. Every request and response is logged in an immutable audit trail, giving compliance teams full visibility into data flow.
LLM03: Supply Chain
The 2025 update expands this category significantly, covering vulnerable libraries, compromised adapters, malicious model merges, and tampered deployments.
How Contextier addresses it:
Contextier’s multi-provider gateway includes failover and fallback logic, ensuring no single model provider represents a single point of failure or compromise. Organizations can route traffic across OpenAI, Anthropic, Azure OpenAI, local models via Ollama, or any combination - based on policy rather than assumption.
The governance layer tracks which models are in use, their versions, and configurations. When a model needs updating or replacement, changes flow through a controlled pipeline with approval workflows, giving security teams visibility and control.
LLM04: Data and Model Poisoning
Data poisoning targets AI system integrity by manipulating training data, fine-tuning inputs, or embedding data. Poisoned models can function normally until a specific trigger activates a backdoor.
How Contextier addresses it:
Contextier’s observability layer monitors production model behavior, tracking output patterns, anomalies, and deviations that could signal poisoning or drift. For enterprises using RAG, the knowledge base and memory systems control what data enters the retrieval pipeline - only verified, policy-compliant sources inform model responses. Data is ingested through governed connectors with source tracking, reducing the attack surface at the retrieval and embedding stage.
LLM05: Improper Output Handling
LLM-generated content passed directly to downstream systems without validation can lead to XSS, SQL injection, remote code execution, and more. OWASP is clear: LLM output should be treated as untrusted input.
How Contextier addresses it:
Every model output passes through configurable guardrails and validation rules before reaching any downstream system. The platform treats every model as an untrusted source by default - a zero-trust approach. For teams building AI-powered automation in financial services or healthcare, an LLM generating a decision cannot bypass the validation layer to execute unauthorized actions.
LLM06: Excessive Agency
As LLMs gain agency through tool use, plugins, and multi-agent architectures, the risk of unauthorized actions grows exponentially. OWASP identifies three root causes: excessive functionality, excessive permissions, and excessive autonomy.
How Contextier addresses it:
Contextier’s governance model is built around least privilege. Every tool invocation in a flow is permission-checked before it fires. Agent-to-agent delegation is scoped and depth-limited. For high-impact actions, human-in-the-loop approval gates can be inserted at any step - routed to specific teams, with expiry and multi-approver support. The policy engine ensures autonomy is granted deliberately, not by default. Every action taken by every agent is recorded in an audit trail.
LLM07: System Prompt Leakage
The 2025 update adds this entry in response to widespread real-world incidents where system prompts containing sensitive configuration, internal rules, or credentials were extracted through straightforward prompt manipulation.
How Contextier addresses it:
Contextier enforces clean separation between sensitive configuration and model instructions. API keys, credentials, and business rules live in the platform’s governance layer with encryption at rest - not in system prompts. Because security controls are enforced independently from the LLM, leaking a system prompt does not leak infrastructure secrets. Guardrails also inspect model output for signs of prompt leakage, adding a detection layer that does not depend on the model’s own compliance.
LLM08: Vector and Embedding Weaknesses
RAG has become the standard approach for grounding LLM outputs in enterprise data. But the OWASP report highlights serious risks: unauthorized access to embeddings, cross-context information leaks in multi-tenant environments, embedding inversion attacks, and data poisoning through the retrieval pipeline.
How Contextier addresses it:
Contextier’s memory and knowledge base systems enforce strict tenant isolation at the vector store level - every collection is scoped per tenant and project. In multi-tenant deployments, there is no shared embedding space where cross-context leakage could occur. Data entering the retrieval pipeline is subject to the same policy and validation rules as any other input. Hybrid retrieval (graph, vector, and full-text) with controlled access ensures that only authorized data informs model responses.
LLM09: Misinformation
LLM hallucinations create real liability. Cases include chatbots providing false legal advice, fabricating court cases, and misrepresenting medical information - leading to lawsuits and regulatory action.
How Contextier addresses it:
Contextier’s evaluation framework enables teams to measure and track output quality in production - using LLM-as-judge, pairwise comparison, and rule-based metrics. For high-stakes applications, outputs can be routed through human approval before reaching end users. The observability layer gives compliance teams real-time visibility into model behavior, making it possible to identify accuracy drift before it results in harm.
LLM10: Unbounded Consumption
The final entry covers denial-of-service attacks, denial-of-wallet attacks, model extraction through excessive API queries, and resource exhaustion.
How Contextier addresses it:
Every API call through Contextier is authenticated, scoped, and metered. Cost attribution tracks usage per tenant, per project, per agent, and per prompt - giving finance and operations teams visibility into consumption patterns. Two-tier LLM caching (exact-match and semantic similarity) reduces redundant model calls. The gateway architecture restricts direct access to model APIs, ensuring extraction attempts are detected and throttled.
From Awareness to Action
The OWASP Top 10 for LLM Applications is essential for understanding what can go wrong. But understanding risks is not the same as mitigating them.
Most enterprises adopting AI bolt security onto workflows never designed for governance. This results in sensitive data in system prompts, unsanitized model outputs hitting production databases, and AI agents with excessive permissions operating without audit trails.
Contextier was built to close that gap. Not as a plugin or afterthought, but as the foundational layer through which all AI workflows flow - orchestrated, governed, observed, and auditable.
If your organization is deploying LLMs in a regulated environment, the question is not whether you need AI governance. It is whether you can afford to operate without it.
Without governance, AI scales risk. Contextier scales control.
For more on how Contextier maps to your compliance requirements, reach out at [email protected].