The LLM Productivity Paradox
A 40-Minute Intelligence Briefing for Pentesters & Researchers on
Turning AI Chaos into a Precision Attack Dominance Engine.
If You’re Using LLMs for Security, You’re Likely Stuck in the “Groundhog Day” Loop.
You open ChatGPT or Claude - put in the prompt - but everytime, it’s the same story.
The LLM doesn’t really know what you’re looking for.
Markdown files is what I personally used to keep these collections of useful info.
But it doesn’t scale well.
Recent versions have some memory capacity, but you know it - it’s not yet there.
And the nature of work we do… it’s somewhat about gaining clarity.
We chase threads - which sometimes lead to a critical vuln, or sometimes dead end, or sometimes a rabbithole.
But how do you tell the LLM - what’s releavant information, what’s outdated.
The paths that you would like to investigate further, and those that it should not explore.
And sometimes the same prompt - they work with GPT-4.1, but the recent Claude Sonnet 4, it’s just a different story. But o3 - you know that one’s different - but the cost of tokens, ah!
What to do? How much time do you spend in sharpening your prompts?
And should you be doing it for all LLMs? But then, where’s the time?
But also if you are not, are you really tapping into the latest model capabilities?
And with generic prompts…
You ask the LLM to “find bugs” and it regurgitates the same generic OWASP Top 10 list.
You spend more time triaging noise than finding novel vulnerabilities.
How much “think deeper” do you keep on saying to the LLMs.
And those responses that are of any significance, what do you do with them?
Your recon notes are in Obsidian, your code snippets are in a text file, your PoCs are somewhere else, and your AI chats are lost in countless browser tabs.
Nothing connects.
You’ve created an information nightmare, not an intelligence asset.
There must be a better way, than just giving up on LLMs.
You know it’s possible.
Here’s the Secret:
The prompt? - It’s important, not the most important!
What you’re really trying to do, is have LLM use it’s compute, to predict, what’s relevant.
To predict it iteratively and explore countless more possibilities than human brains can do.
So the main questions to ask are:
How you can enable LLM to predict what’s most important for you?
What all would it need? How can it handle dynamic information? What should the context look like?
How you can you make it work for any LLM - of today or anytime in the future.
The secret lies in a fundamental shift - in how you interact with LLMs.
And there are two aspects to it: world creation and wayfinding mechanisms.
So you provide them with a carefully crafted world where they predict. It doesn’t matter, whether all the details of the world are already clear or not, it just has to be well-crafted.
Then, you provide them with a way to find their way around that world - a navigation system.
And not just for a single instance, but something repeatable, and in a way that allows fluid communication.
So that you can intervene - because the goal is not to build completely unattended human replacements.
But Enhancers, Accelerators and Augmenters of Intelligence : True Collaborators.
You can use it with Agents, with MCPs, or one-off chats, any LLM, it works.
Get the Blueprint - Reserve Your Spot!
I’ve experimented and continue to experiment with this methodology, this framework, that I’m excited to share with you.
I’ve had great results with it, and so did a handful of other pentesters and researchers I shared this with.
It’s going to be an action packed 40 minutes session - to hand you over the blueprint of what’s possible.
Ultimately, it’s about finding innovative ways to work with tools and technologies.
If you are up for experimentation and trying a new approach to work with LLMs, which might have a little bit of initial learning curve, but once you get it, I can assure you, it’s game-changing.
Well, it’s upto you to decide. Register for the webinar here.
The webinar is absolutely free of cost. Why not give it a try?
- Aditya, Founder, Attify