Smart contracts are immutable programs deployed on blockchain platforms to enable decentralized applications and financial systems without intermediaries. However, their immutability and public execution make them especially vulnerable to security flaws, which can lead to irreversible significant financial losses. A promising approach to improve security is the use of invariants, properties that must always hold during contract execution. FLAMES (Fine-tuned Large Language Model for Invariant Synthesis) is a tool that leverages fine-tuned large language models to automatically generate security-relevant invariants for Solidity smart contracts. Automatically generating and validating such invariants remains a key challenge. This thesis evaluates FLAMES ability to synthesize and validate security-relevant invariants for Solidity smart contracts.