Most security failures in LLM deployments aren't sophisticated attacks—they're preventable mistakes. Here are seven we see repeatedly.
Most security failures in LLM deployments don't come from novel attacks. They come from the same gaps, repeated across organizations at different stages of deployment. Here are seven we see consistently.
1. Not Scanning Open-Source AI Models Before Use
Open-source model files go through far less scrutiny than the prompts sent through them. Deploying a model without scanning it for vulnerabilities first opens the door to backdoor attacks, remote code execution, denial-of-service exploits, and runtime compromise—before a single user interaction happens.
Model weights and serialized files can contain malicious payloads. Scanning at download time and before deployment is not optional.
2. Lack of Security Controls in Production
Input validation, output filtering, harmful content prevention, denied topic detection—these are not nice-to-haves. Skipping them in a production environment creates direct exposure to prompt injection, data leaks, and compliance failures.
The pattern is the same: a team moves fast during development, security controls get deferred, and they stay deferred once the system goes live. By then, the attack surface is open.
3. Insufficient PII and IP Protection
When an LLM handles sensitive data—customer records, internal documents, proprietary processes—inadequate protection for personally identifiable information and intellectual property creates legal and operational risk.
Data anonymization and continuous monitoring are not the same thing. Both are needed. Organizations that rely on one while skipping the other are exposed.
4. Not Performing Red Teaming with Up-to-Date Datasets and Strategies
Threats evolve. Red teaming with last year's attack dataset doesn't catch this year's techniques. Creative prompt injection, subtle data manipulations, and multi-turn exploits require regular testing against current adversarial examples—not a one-time security review at launch.
Red teaming is not a box to check. It's an ongoing process.
5. Text-Only Guardrails for Multi-Modal Applications
If an LLM application accepts images, documents, or audio, but the safety layer only processes text, harmful content in non-text inputs passes through undetected. This gap is common in applications that added multi-modal inputs after the initial security controls were built.
The guardrails need to match what the application actually accepts.
6. Overconfidence in Open-Source Model Security
Open-source doesn't mean audited. The public availability of weights and code is not the same as security review. Open-source LLMs are just as susceptible to prompt injection attacks and internal data leaks as proprietary models.
Assuming otherwise is where the gap gets exploited.
7. No AI Governance Framework
Without a structured governance framework, security decisions are made ad hoc, compliance gets missed, and vulnerabilities go untracked. Ethical lapses, regulatory exposure, and unchecked risks compound over time.
Governance isn't bureaucracy. It's what makes LLM deployments defensible when something goes wrong—and something eventually will.
Promptention helps teams catch these gaps before they become incidents. From red teaming and PII detection to output moderation and governance tooling, we're built for production LLM security.
