When “Local-Only” Isn’t Enough: Lessons from Testing an Enterprise LLM
Think your on-premise LLM deployment is automatically secure? Think again.
The Assessment
Recently, Exfil Security conducted a security assessment for a Fortune 500 company deploying its first fully local large language model (LLM). At first glance, it was an ideal scenario for data privacy:
Management confidently assumed, “If it’s local, it’s locked down.” They asked us to validate that assumption.
What we uncovered should make any enterprise pause before believing that keeping an LLM local equals being secure.
What We Found
Over 20 Security Issues
Our intensive evaluation revealed numerous vulnerabilities. Importantly, these weren’t just novel “AI flaws”—they were a combination of classic web application security weaknesses magnified by new LLM-specific risks.
Here are some highlights:
Attackers could extract internal system prompts, trick the model into producing false or misleading information, encourage harmful outputs, and even manipulate it into role-playing malicious scenarios.
Prompt Injection and Jailbreaking
Any internal user could freely access sensitive files uploaded by users in different departments, with the LLM readily outputting confidential details from those documents.
Access Control Gaps
The application accepted malicious test files without proper validation, opening opportunities for attackers to execute code or plant backdoors.
File Upload & Path Traversal
The LLM occasionally advised users to install software packages that didn’t exist, introducing serious risks of user confusion, mistrust, and potential supply-chain compromise.
Hallucinated Policies
A publicly accessible Git repository exposed internal build scripts, endpoints, and infrastructure details.
Source Code Exposure
The model accepted and returned encoded malware payloads, effectively turning it into a hidden internal malware distribution platform.
Malware Staging
Sensitive Data Echoes
Carefully constructed prompts could trick the LLM into echoing confidential internal information without audit logging, traceability, or accountability.
Key Insight
How Web and AI Vulnerabilities Magnified Each Other
An especially important observation emerged: traditional web vulnerabilities significantly increased the impact of LLM-specific weaknesses, and vice versa. Basic oversights like inadequate input validation, file handling, and authorization gaps allowed attackers to escalate simpler vulnerabilities into serious security breaches.
Our Recommended Security Improvements
Here’s how we advise enterprises to proactively secure their local LLMs:
Implement rigorous prompt and output filtering, token limits, real-time monitoring for abuse, and enforced response timeouts.
1
Deploy a Dedicated LLM Gateway
Clearly define immutable, context-specific instructions that cannot be easily overridden or bypassed by user inputs.
2
Use Robust System Prompts
Don’t underestimate your LLM interface—treat it with the same security rigor as any critical business web application. Apply comprehensive OWASP Top 10 protections.
3
Treat the Front-end as a Mission-Critical Web App
Use validation routines, require confidence scores, and ensure answers involving sensitive topics are carefully reviewed or withheld entirely if the model’s confidence is uncertain.
4
Address Hallucinations Proactively
Establish routine automated testing, including adversarial “prompt storming” and API fuzz testing to catch new vulnerabilities quickly.
5
Continuous Red-Team Testing
Always digitally sign and log every fine-tuning and retraining operation to maintain clear visibility of exactly what model is running at any given time.
6
Model Provenance and Audit
Inline Malware and Endpoint Detection & Response (EDR)
Integrate scanning capabilities to proactively block malicious files or content before they’re ingested or stored by the LLM or related systems.
7
Two Important Takeaways
Lesson 1: “Local” Doesn’t Mean Secure
While hosting an LLM locally reduces some external threats, it does nothing against prompt manipulation, weak internal permissions, or basic web security flaws. Fundamental security practices apply just as much—if not more—to local LLM deployments.
Lesson 2: Cloud vs. Local is a Tradeoff to Consider Carefully.
Established cloud platforms like OpenAI benefit from millions of users continuously testing their guardrails, improving security daily. When hosting your own LLM, you effectively become your own red team, tasked with managing the full burden of detection, prevention, and response. For some organizations, the cloud might actually represent a safer, better-tested environment.
The Bottom Line
Guardrails aren’t optional. Being “local” is no magical defense. If you’re deploying an internal LLM, treat it like any critical enterprise system—because that’s precisely what it is.