I. Introduction:

Large Language Models (LLMs) are powerful artificial intelligence models capable of understanding and responding with human-like language. One notable example is GPT(Generative Pre-trained Transformer) which ChatGPT's GPT-3, 3.5, and 4 are based on.

We have seen an increase in LLM usage across various sectors:

• Healthcare

• Finance and Business

• Education

• Innovation in creativity

• Coding assistance

• Content generation

The decentralised network of the internet (Web3), holds immense promise. However, in its early stage, it has been plagued with a lot of hacks coupled with the loss of billions of dollars.

There are various factors affecting web3 security;

The human factor: developers are also humans and no human is above mistakes, a small coding oversight can lead to significant vulnerabilities.

Interoperability Challenges: Web3's vision includes seamless interactions between diverse blockchain networks. However, this interoperability opens potential vulnerabilities as different protocols and technologies converge.

Smart Contract Sophistication: Smart contracts, the building blocks of Web3, bring unprecedented capabilities but also intricate vulnerabilities. The complexity of these self-executing contracts heightens the risk of coding oversights.

Rapid Evolution and Experimentation: The Web3 landscape is characterised by rapid evolution and experimentation. While fostering innovation, this pace increases the likelihood of overlooking security measures in the quest for progress. We need to foster a security-first mindset (as this is different from the ship fast, fix later mindset in web2) only with this mindset will we be able to reduce hacks.

LLMs emerge as potential game-changers in the dynamic landscape of web3 security, with various promising capabilities that can help secure the blockchain and also prevent hacks from happening.

II. Promising Applications of LLMs in Web3 Security:

Smart Contract Auditing:

There are various limitations in manual auditing that LLMs can help fix Real-time Threat Detection: There are various limitations in manual auditing that LLMs can help fix

• Limited time: as audit is always time-bound, this increases the chances of bugs slipping through to deployment. LLMs can be trained to be able to analyse code and remove bugs ahead before an auditor starts a manual audit.

• Scalability Issues: Traditional auditing struggles with the rapid growth of decentralised applications in the space it becomes harder to fully focus on a project at hand when a firm is packed with a lot of audits.

• Static analysis limitations: Most static analyses make use of pattern matching so they can only catch bugs that their programmers can catch as its core method is using regex or AST. LLMs can replace this as they will be equipped with the capabilities to fully understand what a project entails and start the audit process instead of following a specified pattern as seen in automated bots.

  LLMs can be trained on mountains of code, giving them abilities to analyze and understand programming languages, identify patterns, and sniff out vulnerabilities with breathtaking speed and accuracy Here's how LLMs do it:

1. Code-reading masters: Trained on vast datasets of code, LLMs learn the intricacies of different languages, common coding patterns, and the telltale signs of vulnerabilities. This makes them like seasoned code reviewers who can spot suspicious anomalies even before they become security nightmares.

2. Contextual awareness: LLMs don't just scan code line by line. They grasp the bigger picture, understanding how code blocks interact, how data flows, and how the program interacts with the outside world. This lets them catch vulnerabilities that traditional tools might miss because they lack the context.

3. Adaptive and ever-learning: Unlike static tools with fixed rules, LLMs are constantly evolving. They learn from new vulnerabilities, code patterns, and even developer feedback, making them better at finding even the most obscure security weaknesses.

So, what can LLMs do for us?

• Anomaly detection on steroids: Imagine scanning millions of lines of code in minutes, not days, and having LLMs flag areas that deviate from "normal" coding practices. This lets developers quickly identify potential vulnerabilities, even if they've never been seen before.

• Automated patching: With their deep understanding of code, LLMs can go beyond just finding vulnerabilities. They can potentially suggest fixes, generate secure code patches, and even automate the patching process, saving developers time and resources.

• Proactive security shields: LLMs can be integrated into the development process, analysing code as it's written. This real-time feedback helps developers write secure code from the ground up, preventing vulnerabilities before they even creep in.

Real-time Threat Detection:

Most hacks could have been prevented if users/developers were given a chance to react before the exploit was carried out as most of the hacks have a pattern they follow like Oracle manipulation which is mostly proceeded by a flash loan and a large deposit into the oracle to disrupt the prices, LLM real-time threat detection could pick this up and immediately alert the protocol and potentially prevent the hacks. LLMs can be trained on a massive repository of historical data related to web3 security, including attack patterns, exploit code, and threat intelligence reports. This extensive knowledge base empowers LLMs to understand the modus operandi(MO) of attackers and recognise the indicators of compromise (IOCs) associated with various types of attacks. This real-time monitoring and scoring system offers several advantages:

• Early Detection and Prevention: By identifying suspicious activity as it happens, we can stop attacks in their tracks, minimizing potential damage and protecting users' sensitive data.

• Reduced False Positives: LLMs can differentiate between genuine anomalies and harmless variations in user behaviour, leading to fewer false alarms and less disruption for legitimate users.

• Adaptive and Evolving: LLMs constantly learn from new data, including past attacks and legitimate activity. This allows them to adapt their scoring models and stay ahead of even the most sophisticated attackers.

Social Engineering Protection:

Recently there has been a rise in phishing scams with a new guy in the block address poisoning where a user is tricked into thinking the malicious address is the same as an address they interact with the most by sending fake transactions to them. LLMs can detect phishing scams by recognising the patterns indicating phishing tactics and analysing messages and transactions in real time, providing immediate alerts for potential phishing threats.

III. Conclusion:

As we get to see the success of LLMs in other sectors of life, we can also replicate the success of web3 security making use of their capabilities to analyse vast codes, learn from past attacks and be able to prevent possible exploits in codes and provide a real-time threat detection to secure the decentralised world.

Imagine a Web3 where:

• Real-time anomaly detection: LLMs monitor every transaction, and every smart contract interaction, sniffing out fraudulent activities before they can inflict harm.

• Adaptive threat prevention: Learning from each attack, LLMs evolve their defences, staying ahead of even the most sophisticated adversaries.

• Personalized user warnings: Forget generic alerts. LLMs provide contextual warnings, empowering users to make informed decisions about their digital assets and interactions.

  All these are what AEGIS AI is providing to help secure the blockchain security and making web3 a place where everyone can interact, innovate and thrive safe from the shadows of bad actors. Let’s harness the potential of LLMs.