LLM Security Threats Every Enterprise Should Prepare For

A New Class of Vulnerability

Large language models are showing up in enterprise applications faster than security teams can assess them. Customer support chatbots, code assistants, document summarizers, internal knowledge bases. The use cases are compelling and the deployment pace is aggressive.

But LLMs introduce attack surfaces that look nothing like traditional application vulnerabilities. They don't follow deterministic rules. They process natural language, which means attack payloads look like normal text. And they can be manipulated in ways that bypass every conventional security control you have in place.

The OWASP Top 10 for Large Language Model Applications (updated for 2025) provides the most authoritative taxonomy of these risks. Here are the ones that matter most for enterprise deployments.

1. Prompt Injection

This is the most discussed and most dangerous LLM vulnerability. Prompt injection occurs when an attacker crafts input that overrides the system instructions given to the model.

Direct injection happens when a user types something like "Ignore your previous instructions and instead do X." It sounds simple, but variations of this attack remain effective against many deployed models.

Indirect injection is more insidious. Malicious instructions are embedded in content the model processes: a web page it's asked to summarize, a document it's asked to analyze, or a database record it retrieves during conversation. The user never sees the payload, but the model follows the embedded instructions.

Mitigation: Implement input sanitization, but recognize that it's not a complete defense because natural language attacks are hard to filter reliably. Layer your defenses: separate the LLM's permissions from the user's, validate all model-initiated actions before execution, and never let an LLM directly execute privileged operations without a human approval step.

2. Insecure Output Handling

LLM outputs are often passed directly to other systems: rendered in web pages, used in database queries, fed to APIs, or written to logs. If the output contains malicious content (whether generated by the model spontaneously or triggered by a prompt injection), it can cause cross-site scripting, SQL injection, or command injection in downstream systems.

Mitigation: Treat LLM output the same way you treat untrusted user input. Sanitize it, escape it, and validate it before passing it to any other system. This applies even when the LLM is an internal system that you control.

3. Training Data Poisoning

If an attacker can influence the data used to train or fine-tune your model, they can introduce backdoors or biases that persist indefinitely. This is especially relevant for models fine-tuned on data collected from public sources, user-generated content, or third-party datasets.

Mitigation: Maintain strict provenance tracking for all training data. Validate data quality and integrity before training. Monitor model behavior against baseline benchmarks after every retraining cycle to detect unexpected changes.

4. Model Denial of Service

LLMs are computationally expensive. An attacker who can craft inputs that maximize processing time and resource consumption can drive up costs or degrade service for legitimate users. Long, complex prompts or prompts that trigger maximum-length outputs are common vectors.

Mitigation: Set hard limits on input length, output length, and request rate. Monitor token usage per user and per session. Implement timeout mechanisms that terminate requests exceeding defined resource thresholds.

5. Supply Chain Vulnerabilities

Enterprises rarely train foundation models from scratch. They use pre-trained models from OpenAI, Anthropic, Meta, Mistral, or Hugging Face. They add plugins, tools, and retrieval-augmented generation (RAG) components. Each dependency is a potential attack vector.

A compromised model, a backdoored open-source library, or a vulnerable plugin can introduce risks that are invisible to your application code.

Mitigation: Maintain a comprehensive inventory of all AI components, including model versions, libraries, and external APIs. Pin versions. Monitor security advisories. Validate model behavior against known benchmarks before and after any update. Apply the same supply chain security practices you use for traditional software.

6. Excessive Agency

When LLMs are connected to tools that can take actions (sending emails, querying databases, calling APIs, modifying files), the blast radius of any vulnerability increases dramatically. A prompt injection that would otherwise just produce wrong text can now trigger real-world actions.

Mitigation: Apply the principle of least privilege to LLM tool access. Only grant the model the minimum permissions needed for its intended function. Require explicit user confirmation for high-impact actions. Log all tool invocations for audit purposes.

7. Sensitive Information Disclosure

LLMs can leak sensitive information in several ways: reproducing training data fragments, exposing system prompts, revealing internal tool names and API endpoints, or including sensitive context retrieved during RAG in their responses.

Mitigation: Carefully audit what information is accessible to the model. Implement output filtering that detects and redacts sensitive data patterns before returning responses. Regularly test with adversarial prompts designed to elicit system prompt disclosure or data extraction.

Building an LLM Security Strategy

These threats are not theoretical. They are being actively exploited in the wild. An effective enterprise LLM security strategy includes:

Threat modeling for every LLM-based application before deployment.
Layered defenses that combine input validation, output sanitization, permission restrictions, and monitoring.
Red team testing with security professionals who specifically attempt prompt injection, data extraction, and tool misuse.
Monitoring and alerting on LLM-specific signals: unusual prompt patterns, unexpected tool usage, anomalous output content.
Incident response procedures that cover LLM-specific scenarios (see our article on updating your IR playbook for the AI age).

Moving Forward

The organizations deploying LLMs most safely are the ones treating them as a new application category that requires its own security practices, not an extension of existing web or API security.

Review the OWASP Top 10 for LLMs with your security team. Map each risk to your current LLM deployments. Identify gaps. And build security into your AI development lifecycle from the start, not as a retroactive assessment after the application is already live.