Welcome back, aspiring cyberwarriors!

Every OSINT investigator now has access to a large language model on their desktop. This tool can quickly summarize thousands of pages or organize chaotic names and dates into a clear timeline. And this is widely accepted. But the difference between an investigator who gets useful results and one who gathers unhelpful information lies not in the model they use, but in how they interact with it.

This article explains how to create prompts that improve your OSINT work. It draws on recent academic research about prompting strategies for security tasks and insights from experienced OSINT practitioners. Let’s get rolling!

Prompt Is More Important Than the Model

Recent research on large language models in offensive security tasks highlights an important point. Methods that relied on reasoning, such as few-shot and chain-of-thought prompting, produced better results. These methods generated code that closely matched the reference examples, executed correctly, and handled various situations well.

One surprising finding was that continuously refining a prompt or asking the model to rethink its answer often resulted in worse outcomes. This happened because rephrasing the prompt misinterpreted the original task and lowered the accuracy of the output. So, repeatedly saying “try again” or “are you sure” is not an effective debugging strategy. If your first prompt was unclear, it is better to follow up with a clear and structured prompt rather than trying to steer the model to a better answer afterward.

Focus on crafting clear instructions at the beginning.

Create a Master Prompt Before You Begin

Before discussing a specific OSINT case, it’s a good practice to create a master prompt. This set of rules, though not related to the target, guides how the model should perform during the session.

This approach uses a well-known idea called anchoring. The first instructions you give to a model have a big influence on all the following interactions. If your first message includes a leading assumption, the model will consider that assumption important for the rest of the conversation, even if you didn’t mean for it to be.

A strong master prompt might look something like this.

Act as a neutral OSINT analyst. Do not treat any hypothesis as proven. Separate facts, indicators, assumptions, and conclusions. Look for evidence against a theory as actively as evidence for it. If the available data is insufficient, say so directly instead of filling the gap.

In other words, this is the initial set of instructions that the AI will follow throughout the entire time you’re working on the task. This reduces the number of errors because it explains to the model in advance exactly how to proceed and which rules to follow.

Basically, a master prompt should specify:

the role;

the goal of the task;

neutrality;

verification rules;

the response format;

a prohibition on unconfirmed conclusions and fabrications;

a requirement to distinguish facts from hypotheses;

a requirement to indicate what is missing.

And then you can set a specific task: what you’re looking for, who you’re looking for, what time period you’re covering, what sources you already have, and so on.

Ask Neutrally

One important but often overlooked issue in AI-assisted open-source intelligence is sycophancy. This happens when a model tends to agree with what the user already believes. For example, if you ask, “prove that this person is connected to this company,” the model starts with the answer you want before it even begins its search. It will look for information that confirms your belief simply because that’s what you asked for.

To fix this, you need to change how you phrase your requests. Instead of using words like “prove,” “confirm,” or “expose,” use “check” or “assess.” Don’t present your suspicions as facts. Phrases like “I already know” or “I am certain” lead the model to validate your beliefs rather than question them. Instead, ask it to build arguments for and against your theory, suggest different explanations, and counter your own ideas.

Specific Prompts You Can Use in Your Own Investigations

Theory is useful, but what most investigators actually want is something they can paste into a chat window tonight. Below are working examples for common OSINT tasks.

For digging into a person’s background, a strong prompt reads something like this.

Act as a neutral OSINT analyst. I am researching a public figure named [name], active in [industry or region]. Using only the material I provide below, build a table of confirmed facts with a source for each one. Separate anything that is an inference or a pattern from anything that is a documented fact. Flag contradictions between sources. Do not draw a conclusion about the person's character or intentions, only report what the material actually supports.

For sorting through a pile of scraped social media posts or forum threads, the goal is structure and pattern detection rather than interpretation.

Read the following set of posts and extract every name, date, location, and organization mentioned. Group them into a timeline ordered by date. Note any account that repeats phrasing found in another account, since that may indicate coordinated rather than organic activity, but do not conclude that it is coordinated, only flag it for review.

For checking a corporate or business connection, the earlier example from this article works well as a template, but it is worth repeating in full because it demonstrates every rule at once.

Assess a possible connection between [subject] and [company] between 2020 and 2025. Use only the material provided. Produce a table with columns for fact, source, supporting detail, confidence level, and what still needs verification. Search for evidence against the connection as thoroughly as evidence for it. List the weak points in the theory separately at the end.

For translating and analyzing foreign language material, always ask for the original text alongside the translation, since that preserves your ability to verify tone, sarcasm, and slang later.

Translate the following text into English. Preserve the original text beside the translation. If any phrase relies on slang, sarcasm, or local idiom that may not translate directly, mark it and explain the likely intended meaning separately from the literal translation.

For comparing multiple documents or reports covering the same event, the model’s strength is spotting overlap and divergence quickly, provided you tell it not to resolve the divergence for you.

Compare the following three reports about the same event. List every claim that all three sources agree on, every claim only one source makes, and every direct contradiction between them. Do not decide which source is correct, only present the disagreement clearly.

And for stress testing your own working theory before it goes into a report, a short adversarial prompt catches more mistakes than another hour of reading.

Here is my working theory: [state theory]. Argue against it as convincingly as you can, using only the evidence already provided in this conversation. Then list what additional evidence, if it existed, would be needed to make the theory solid.

Summary

Carefully written prompts that encourage reasoning are more effective than careless, repetitive prompts. Another important point is that a claim from the model is just a suggestion, not a fact, until you check the source yourself. Even as the tool improves, it’s still your job to decide what is a true finding and what is just a coincidence.

We also invite you to join our AI for Cybersecurity training. During the training, we’ll show you different ways of using AI in cybersecurity, set up local models and solve labs. The field is evolving rapidly and the sooner you learn things, the greater the advantage you’ll have. There’s no reason to resist AI. It’s a tool to master.

The post Artificial Intelligence (AI) in Cybersecurity, Part 24: Prompts That Will Supercharge Your OSINT Research first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Many software-as-a-service (SaaS) platforms are adding AI chatbots to their products. But these bots do more than just chat; they can access internal tools and perform tasks for users. Each of these actions creates a potential security risk, and developers are releasing these features faster than they can secure them.

In this article, I want to share a type of vulnerability I found while testing an AI chatbot – LLM02:2025 Sensitive Information Disclosure – which allowed me to obtain users’ first and last names and email addresses just by interacting with the chatbot. Let’s get rolling!

What is LLM02:2025 Sensitive Information Disclosure?

LLM02:2025 Sensitive Information Disclosure is a critical security issue listed in the OWASP Top 10 for LLM Applications 2025. This problem occurs when a Large Language Model (LLM) application accidentally reveals confidential or personal information in its responses.

This vulnerability happens because LLMs are designed to be helpful and use all available context, such as training data, system prompts, and runtime inputs, to create replies. If sensitive information is included in these sources without proper protections, the model may accidentally disclose information it should keep private.

Step 1: Find the Hidden Feature Behind the Feature

Our target had a chat feature that looked, on its face, completely trivial: type an arithmetic expression wrapped in double curly braces, and the bot evaluates it and replies with the answer.

{{5-5+0}} = 0
{{5*5+0}} = 25

Just like a calculator. But the interesting bugs live in the features nobody thought to test twice. So I started varying the input systematically, the way you’d fuzz any parameter in a pentest. And the calculator started talking back with things that were most definitely not numbers:

{{5-0+0}} → collectionPropertyOption://CollectionName
{{1-0+0}} → https://domain/p/CollectionName
{{2-0+0}} → user://UUID

This shows that the “calculator” is really a thin cover for a much riskier part of the code, which allows direct access to the platform’s internal system without any authentication.

Step 2: Isolate the Real Trigger

When you notice a leak like this, do not just note it down. Understand what is causing it.

My first guess was that the final answer of the expression is used as a lookup index. This is easy to test. Try expressions that all result in the same number through different math operations:

{{2+2}}    = 4        (plain number)
{{2+2+0}}  = 4        (plain number)
{{2-2+0}}  = user://UUID    ← NOT a plain number!

That last result contradicted my guess. The expression 2-2+0 simplifies to 0, but the result came back as an object reference instead of a plain number, similar to when the expression equals 2. This was a clue. I ran a few similar tests to confirm:

{{4-2}}    (=2)  → collectionProperty:// reference (type 4, not type 2)
{{5-2+0}}  (=3)  → collectionPropertyOption:// reference (type 5, not type 3)
{{0+2-2}}  (=0)  → plain number (type 0)

In conclusion, the lookup key is based on the first operand’s type, not the final arithmetic result. The developer’s evaluator takes the first number from the input and puts it directly into an internal array, sorted by object type, completely separate from what the expression calculates. I confirmed this by testing the limits:

{{99-2+0}}              = 97 (plain math, outside the enum)  
{{99999999999-0+0}}     = 99999999999 (same, safely out of range)

Step 3: Turn the Reference Into a Record

The next question is obvious: will the system actually resolve that reference if I hand it back to it?

Yes. It will.

{{user://<uuid>}} → returns first name, last name, and email address of that user.

This AI chatbot successfully provided information about any user by knowing his UUID.

Summary

Finding an issue like this, such as index leak, reference resolution, and PII disclosure, is not just luck; it is a systematic approach. This is the practical skill we teach at Hackers-Arise. As AI systems become part of every platform you will test, hackers who understand these new areas of attack will have the best tools and the highest salaries. Check out our Subscriber training package to start building these skills effectively and methodically.

The post Hacking: How a “Calculator” Feature Can Leak Internal Object References (and Real User PII) in AI Chatbots first appeared on Hackers Arise.