Top GPT Tools in Cybersecurity: Offensive vs Defensive AI for Ethical Hacking and Blue Teaming

Key takeaways

• Sort every tool into three groups first: vendor security copilots, general models like ChatGPT and Claude, and criminal dark LLMs such as WormGPT.
• Never paste secrets, client data or credentials into a general model when analysing logs, and review its output before acting.
• Treat WormGPT and FraudGPT as threats to understand and defend against, since adopting criminal tools crosses a legal line.

Search for "GPT hacking tools" and you get a mix of real security products, custom GPTs of unknown quality, and outright scams sold on criminal forums. Knowing which is which matters, because the wrong answer either wastes money or crosses a legal line. Below, the tools are grouped by what they actually are, what they do, and how a defender treats each one.

What are the three kinds of "cybersecurity GPT" you will meet?

There is no single category. Before you trust any tool, place it in one of these buckets, because the rules are different for each.

Defensive AI tools that are worth a real look

These are the tools a blue team or SOC analyst can actually buy and run. They do not replace analysts; they cut the time spent on repetitive triage and writing.

• Microsoft Security Copilot sits on top of Microsoft Sentinel and Defender and turns raw alerts into a plain-language incident summary, with suggested next steps an analyst can accept or reject.
• CrowdStrike Charlotte AI answers questions about detections in the Falcon platform and helps prioritise which alerts to work first.
• Google's Gemini-based security tooling (built on the Mandiant and Chronicle stack) helps with threat summarisation and detection engineering.
• SOC analyst agents from vendors like Proofpoint now use OpenAI's security-tuned models to speed up investigation. These are new, so measure them against your own data before trusting the output.

The common thread: each is tied to a data source the vendor controls, which keeps answers grounded. For broader context on where these fit, see how generative AI can be used in cyber security and the wider set of generative AI use cases in cybersecurity.

Using general models (ChatGPT, Claude) safely for security work

You do not need a special "hacking GPT" for most day-to-day help. A general model, used carefully, covers a lot of legitimate ground.

• Explaining an unfamiliar log line, error or configuration
• Drafting detection logic, then testing it yourself before it goes live
• Summarising a long threat-intel report into action items
• Writing and reviewing automation scripts for your own environment
• Learning concepts and building study plans for certifications

Three rules keep this safe. Never paste credentials, customer data or internal IP addresses into a public model. Treat every output as a draft to verify, because models still invent plausible-looking commands and CVE numbers. And keep a human decision between the model's suggestion and any change to a production system.

WormGPT, FraudGPT and the "dark LLM" claims

These are not tools you should seek out. They matter because attackers use them, and because the market around them is full of scams. Here are the verifiable facts.

• WormGPT appeared on a hacking forum in mid-2023 as a "no limits" model aimed at business email compromise. Reporting indicates the original seller stopped sales later that year.
• FraudGPT was marketed around the same time for phishing and fraud. Its sale threads have since largely disappeared.
• Most named "successors" (EvilGPT, WolfGPT and similar) have been exposed as scams or as thin wrappers around jailbroken mainstream models that still refuse illegal requests.
• The real shift is attackers trying to jailbreak legitimate models with crafted prompts rather than building their own criminal LLMs.

For a defender, the takeaway is not the tool name. It is that AI lowers the effort for convincing phishing and fraud at scale. That is a detection and awareness problem, covered next.

How do defenders respond to AI-assisted attacks?

The defence does not change because the attacker used a model. It gets more important. Focus on outcomes, not on the tool behind them.

Caution for learners: experiment with any security tool only on systems you own or are explicitly authorised to test. In India, unauthorised access and related activity fall under the Information Technology Act, 2000, and penetration testing without written permission can be an offence regardless of intent.

How do you choose a tool for your role?

Match the tool to the job rather than chasing a "top 10" list.

• SOC analyst or blue teamer: start with the AI features already in your SIEM or EDR, then add a dedicated copilot if it measurably cuts triage time.
• Student or career changer: use a general model plus hands-on labs. The skill that employers want is judging the output, not prompting it.
• Red teamer or pentester: use models to speed up documentation, reporting and your own tooling, inside scoped and authorised engagements only.

If you want this grounded in a structured path, the Certified SOC Analyst programme and the AI and LLM security course cover both the defensive tooling and the risks of the models themselves.

A sensible next step

Pick one real task this week, such as summarising your last incident or reviewing a detection rule, and run it through a tool you can account for. Compare the output against your own analysis. That habit, of treating AI as a fast but fallible assistant whose work you verify, is what separates teams that benefit from AI from teams that get burned by it.

Related reading

• Ethical Hacking with OffensiveGPT | A Game Changer? How AI-Powered Ethical Hacking, Automated Penetration Testing, and AI in Cybersecurity Are Transforming Threat Detection and Vulnerability Assessment
• What Is HackGPT? How Can It Enhance Cybersecurity and Ethical Hacking?
• What is The Rise of AI-Powered Red Teaming: Automating Attacks with Large Language Models in Cybersecurity?