Data Leakage in LLM Fine-Tuning
Is your company fine-tuning language models with proprietary data to improve responses? A 2026 Check Point Research report reveals that 68% of Brazilian companies fine-tuning LLMs do not implement basic data isolation measures.
The problem is serious: customer data, business strategies, and financial information are incorporated into models without proper protection. And the legal responsibility, according to the LGPD, remains yours.
In this article, you will discover the five most overlooked security risks when fine-tuning LLMs. And, more importantly, how to mitigate them.
1. Data Leakage in Shared Models
When you fine-tune a model on a shared platform, your training data can be exposed to other users. This happens because many providers do not adequately isolate fine-tuning data between clients.
In 2025, Samsung suffered a serious incident: engineers sent proprietary source code for fine-tuning on a public model, and the information was incorporated into the base model. The company had to ban the use of the tool.
"Data sent for fine-tuning on public models can be incorporated into global training and exposed in future responses. Companies should treat these systems as public channels." — Statement by Gil Shwed, CEO of Check Point, in an interview with Reuters in January 2026
The solution? Demand that the provider offer an isolated instance of the model, where your fine-tuning data is not shared with global training. Many providers already offer this option, but it costs more.
2. Lack of End-to-End Encryption
Most fine-tuning services encrypt data in transit (HTTPS), but not all guarantee encryption at rest. And, worse, many process data without encryption during training.
This means that during fine-tuning, your data is exposed in the provider's server memory. An attacker with system access could read sensitive information.
According to Cloudflare, only 35% of fine-tuning providers offer complete end-to-end encryption. The rest rely on traditional security measures that may be insufficient.
How to protect yourself? Check the contract to see if the provider offers homomorphic encryption or trusted execution environments (TEE). These technologies allow data to be processed without decrypting it.
3. LGPD Compliance at Risk
The Brazilian General Data Protection Law (LGPD) is clear: the data controller (your company) is responsible for any leakage, even if it occurs at the processor (the fine-tuning provider).
Many fine-tuning contracts have clauses that transfer responsibility to the client. This is illegal in Brazil. The LGPD does not allow responsibility to be contractually transferred.
A landmark case occurred in 2025: a Brazilian fintech had data from 2 million customers leaked during a fine-tuning process at a third-party provider. The ANPD fine was R$ 15 million, and the fintech could not absolve itself of blame.
The recommendation is clear: demand that the contract complies with the LGPD and that the provider signs a joint liability agreement. If they refuse, look for another supplier.
4. Prompt Injection Attacks on Fine-Tuned Models
Fine-tuned language models are vulnerable to prompt injection attacks. A malicious user can manipulate the model to execute unauthorized actions or leak fine-tuning data.
For example, a customer service chatbot fine-tuned with customer data could be induced to reveal other users' information if the prompt is manipulated correctly. This has already happened with telecommunications companies in the US.
Prompt injection attacks are difficult to detect because they exploit the very nature of the model. Traditional security tools cannot block them.
Defense requires layers: input filters that validate prompts, context limits that prevent access to data outside the session, and continuous auditing of suspicious responses.
5. Dependence on Providers Without Certifications
Many fine-tuning providers lack recognized security certifications, such as ISO 27001 or SOC 2. This means you have no guarantee they follow best practices.
A 2026 Gartner survey shows that 40% of fine-tuning providers have no security certification. And 25% of them have experienced security incidents in the last two years.
The problem is that without certification, you cannot audit the provider. And if a leak occurs, the responsibility falls on your company.
The solution is simple: only hire providers with up-to-date certifications. Demand copies of audit reports. If the provider refuses to provide them, consider this a red flag.
Comparative Table: Security by Provider
| Provider | End-to-End Encryption | Data Isolation | Certifications | LGPD Compliance |
|---|---|---|---|---|
| AWS (Bedrock) | Yes (TEE) | Yes | ISO 27001, SOC 2 | Yes |
| Google Cloud (Vertex) | Partial | Yes (optional) | ISO 27001, SOC 2 | Yes |
| Azure (OpenAI) | No | Yes (Enterprise) | ISO 27001, SOC 2 | Partial |
| Local Providers | Variable | Variable | Rare | Variable |
Source: Official provider documentation and Gartner 2026 report.
The table shows that global providers offer more guarantees, but gaps still exist. Local Brazilian providers may be more flexible but lack certifications.
How to Build a Security Strategy for Fine-Tuning
The first line of defense is due diligence. Before hiring, audit the provider. Check certifications, privacy policies, and incident history.
The second is data segmentation. Never send sensitive data for fine-tuning on public models. Create an anonymization layer that removes personal information before processing.
The third is continuous monitoring. Set up alerts to detect suspicious usage patterns. Many platforms offer audit logs showing who sent what and when.
Finally, invest in training. Your employees need to know that fine-tuning is not a trivial process. Every piece of data sent can be a security risk.
Conclusion
Fine-tuning LLMs is a powerful tool, but it does not come with built-in security guarantees. The risks of leakage, lack of encryption, and non-compliance with the LGPD are real and can be costly.
The secret lies in governance. Audit, segment, monitor, and train. Each risk can be mitigated with a well-drafted contract and a robust security strategy.
The companies that will succeed with fine-tuning in 2026 will not be those that adopt it fastest. They will be those that adopt it securely. And responsibly.
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