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05/07/2026 6:53 pm
TL;DR: Fine-tuning improves task performance, but it does not remove prompt injection, data poisoning, or deployment-time security risks in LLM applications, according to Lakera's analysis. The real control question is whether teams are governing model behaviour, data handling, and guardrails with the same discipline they apply to access and secrets.
NHIMG editorial — based on content published by Lakera: The Ultimate Guide to LLM Fine Tuning: Best Practices & Tools
Questions worth separating out
Q: What security risks remain after fine-tuning an LLM?
A: Fine-tuning can improve task accuracy, but it does not remove prompt injection, data poisoning, unsafe outputs, or access risks in the surrounding pipeline.
Q: Why do fine-tuning pipelines create NHI governance issues?
A: Fine-tuning pipelines usually depend on service accounts, storage systems, and automation jobs that move data and model artifacts between environments.
Q: How should teams decide whether to fine-tune or use prompt-based approaches?
A: Teams should choose the simplest approach that meets the use case and security requirements.
Practitioner guidance
- Separate training trust from runtime trust Review whether your fine-tuning process includes untrusted prompts, external data, or shared credentials that could still influence production behaviour after deployment.
- Classify fine-tuning data as governed content Apply access controls, retention limits, and review procedures to datasets, checkpoints, and evaluation outputs the same way you would for other sensitive operational assets.
- Harden the non-human identities around the pipeline Inventory service accounts, API keys, and automation roles used in model training and delivery, then remove standing access that is broader than the job requires.
What's in the full article
Lakera's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step explanation of fine-tuning phases, including training, validation, testing, and deployment choices.
- Practical comparisons of fine-tuning methods and the trade-offs between speed, cost, and model behaviour.
- Tooling references for model development workflows, including libraries and training platforms mentioned by the vendor.
- Discussion of common limitations such as overfitting, domain shift, and unintended outputs.
👉 Read Lakera's guide to LLM fine-tuning best practices and tools →
LLM fine-tuning security gaps: are your controls keeping up?
Explore further
05/07/2026 7:13 pm
Fine-tuning improves usefulness, but it does not erase the security model the application sits inside. A tuned model may be more accurate on a narrow task, yet it still consumes untrusted input and can still be manipulated through prompt injection or poisoned examples. The practical lesson is that training quality and operational trust are separate questions, and the latter remains unresolved after fine-tuning.
A few things that frame the scale:
- 71% of NHIs are not rotated within recommended time frames, increasing the risk of compromise over time, according to the Ultimate Guide to NHIs.
- A separate finding from the same research shows that 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
A question worth separating out:
Q: How do you know if a fine-tuned model is operating safely in production?
A: Look for evidence that the model is being monitored after deployment, that sensitive inputs are filtered, that outputs are reviewed where needed, and that access to the pipeline is tightly scoped. If those controls are missing, the model may be accurate but still unsafe. Safe operation is a control outcome, not a training claim.
👉 Read our full editorial: LLM fine-tuning still needs security controls beyond accuracy
