How to Automate Software Reverse Engineering with AI: Tools, Approaches, and Practical Examples

Key takeaways:

• AI can partially automate reverse engineering by analyzing decompiled code, suggesting meaningful names, and helping to explain complex logic.
• Your team can use AI manually, through plugins, or via MCP frameworks for deeper integration.
• Privacy and legal compliance are critical, and sensitive code should never be shared with external AI services.
• AI outputs are not always accurate, so every insight should be verified by an experienced reverse engineer to ensure reliability.

Reverse engineering software is a meticulous, iterative process with many steps and constant back-and-forth — a level of complexity that often overwhelms AI tools. Many teams have already tried using AI for reverse engineering only to be disappointed by vague answers or errors that take more time to fix than the AI tools save.

AI isn’t a magic solution for reversing, but it can support specific parts of the workflow when used thoughtfully. It works best on repetitive tasks such as analyzing decompiled code or identifying functions, allowing engineers to focus on higher-level reasoning and decision-making.

In this article, we take a practical look at how you can automate your reverse engineering with AI — particularly, with large language models (LLMs). We’ll walk you through concrete examples, discuss common pitfalls and limitations, and show where AI adds value — and where traditional manual analysis remains the better choice.

AI in reverse engineering — yes or no?

Typically, an engineer starts with an unfamiliar binary and spends a significant amount of time decompiling code, navigating poorly named functions, reconstructing program logic, identifying algorithms, and forming hypotheses about what the software does. This is why quality reverse engineering takes so much time.

However, while this process requires human judgment, a large portion of the work is repetitive and pattern-based. This means you can automate or speed up some tasks with the help of AI.

Where AI can be applied in reverse engineering

AI works best when applied to analyze decompiler output. Decompiled code shares many properties with natural language: it is structured, context-dependent, and pattern-based. 

Since modern machine learning architectures were originally developed for natural language processing, they are well-suited to working with this type of data. 

Note that there’s usually no need to train a dedicated model, as a general-purpose LLM is enough for basic reverse engineering tasks.

Reverse engineering tasks AI can help you with include:

• Understanding decompiled code. AI assistants can explain the purpose of functions, control flow, or suspicious logic in natural language during the initial “what does this do?” phase.
• Identifying and naming program elements. Functions, variables, and data structures can be renamed based on context and usage patterns, making large volumes of compiler-generated identifiers easier to work with.
• Recognizing patterns. AI can identify common constructs such as cryptographic routines, validation logic, serialization code, or state machines faster, without the need to manually rediscover known patterns.
• Assisting in navigation and documentation. AI can help locate cross-references, related functions, or call graphs, allowing engineers to focus on deeper analysis instead of tooling mechanics.

While AI does not replace the reverse engineer, it compresses some of the most time-consuming parts of the workflow, enabling analysts to understand raw decompiler output much faster.

But as with any technology, AI has its limitations. Let’s see what reverse engineering tasks are best left to human reverse engineers (at least for now).

Where AI performs poorly and manual analysis is still required

AI struggles with tasks that require deep semantic understanding, implicit assumptions, or absolute precision. Manual analysis by an experienced reverse engineer is the better choice when you are dealing with:

• Complex algorithms
• Heavily obfuscated logic
• Edge-case-driven behavior
• Security-critical decisions

In all of these cases, AI is insufficient for one reason: AI models don’t reason about software the way experienced reverse engineers do. They infer meaning from patterns in decompiled code, but they don’t truly understand intent, execution context, or correctness requirements.

Instead of understanding, AI fills in the gaps probabilistically, which can introduce errors and hallucinations.

For this reason, you should always treat AI output as a hypothesis that requires verification, not as a final answer.

Approaches to AI-assisted reverse engineering

At Apriorit, we have tested several ways to apply AI to reverse engineering tasks. 

The simplest approach is to manually feed decompiler output (usually snippets of pseudocode) to an AI assistant and ask for explanations of complex or obfuscated logic. This method already saves time and is commonly used in exploratory analysis. 

A more advanced approach is to integrate AI directly into a reverse engineering tool such as IDA or Ghidra using plugins. These integrations allow decompiler output to be automatically analyzed and contextualized without leaving the reversing environment.

You can even go one step further and allow AI to interact directly with a decompiler’s internal database. Using frameworks such as Model Context Protocol (MCP), AI agents can perform actions that an engineer would normally do, such as:

• Searching for functions
• Renaming symbols
• Running decompilation
• Retrieving cross-references 

By chaining these actions together, the AI can autonomously analyze larger portions of program logic.

Now, let’s move from theory to practice. In the next sections, we’ll walk through three practical hands-on examples based on the last two AI reverse engineering approaches discussed above. They will focus on concrete workflows supported by tools like:

• IDA Pro
• Gepetto
• GPT
• Ghidra
• Ghidra MCP
• PyGhidra

Our guide will be most useful for reverse engineers and engineering teams who want to understand how to apply AI in real reversing tasks.

If you’re approaching this topic from a business perspective and are mainly interested in conclusions and practical takeaways, you can skip ahead to the challenges and recommendations section where we summarize what these examples mean in practice.

Practical example 1: Using the Gepetto plugin with IDA for static reverse engineering

In this example, we’ll be using:

• IDA Pro (7.4+)
• Gepetto (v1.4.1) 
• OpenAI’s GPT models (specifically, Gepetto’s default GPT-4o) 

The idea here is to simply forward the decompiler’s output to the GPT model so that it either returns an explanation of the decompiled function or renames all variables to make them more descriptive. 

To demonstrate this, we’ll use an unpacked test application [.exe] file that we first mentioned in this article. More specifically, we’ll take a look at its TLS callback function that’s supposed to detect whether a debugger is attached to the process. 

This is what the original looks like:

First, let’s choose Gepetto in the context menu:

In this demonstration, we’ll use the Explain function feature. It will append a comment on top of the decompiled function’s pseudocode like this:

Here’s the full version of the comment:

—– Comment generated by Gepetto —– 

The provided function appears to be a Thread Local Storage (TLS) callback function that is designed to detect if the process is being debugged. Here’s a breakdown of what the function does: 

1. **Initial Setup:** 

– The function takes three integer arguments `a1`, `a2`, and `a3`, which aren’t actually used within the function body. 

– A local variable `v3` is initialized to the value of `byte_40443C`. `byte_40443C` seems to be a global or static variable that is presumably used to store a debugging status flag. 

2. **Debugging Check via PEB:** 

– The `BeingDebugged` field of the Process Environment Block (PEB) is checked. This field is set to a non-zero value if a debugger is attached to the process. 

– If `BeingDebugged` is true, `v3` is set to `1`. 

3. **Process Debug Port Check:** 

– `NtQueryInformationProcess` is called to query the `ProcessDebugPort` for the current process (denoted by `(HANDLE)0xFFFFFFFF`, meaning the current process itself). 

– This function will store the result in`ProcessInformation`. A non-zero `ProcessInformation` usually indicates the presence of a debugger. 

4. **Setting the Debugging Flag:** 

– If `NtQueryInformationProcess` succeeds (`result` is `0`), the function evaluates whether `ProcessInformation` is non-zero. 

– If `ProcessInformation` is non-zero, indicating a debugger is present, `byte_40443C` is set to `1` (debugger detected). 

– Otherwise, it maintains its previous value based on the initial PEB check. 

5. **Return:** 

– The function returns `1` if a debugger is detected (from either source), otherwise it returns `0`. 

### Suggested Better Name: 

Given the functionality, a more descriptive name for this function might be `DetectDebuggerTlsCallback`. This name indicates that the function is a TLS callback involved in detecting whether a debugger is present, which closely aligns with its actual purpose. 

—————————————- 

Gepetto correctly identified the function, which is good. Now, let’s see how it deals with renaming variables. For this, we’ll select the second feature, Rename variables.

As a result, the Gepetto tool has successfully renamed a few elements. For example, v3 turned into debugFlag. Now this piece of code is much easier to analyze.

Note that global variables such as byte_40443C aren’t supported by Gepetto, so they remain as is. But even with these limitations, automated renaming made the code far easier to understand and reduced the time needed for manual analysis.

Practical example 2: Using the original Ghidra MCP for static reverse engineering

In this example, we’ll use the following tools: 

• Ghidra 11.3.2 
• Ghidra MCP 1.4 server
• 5ire MCP client 
• OpenAI’s GPT models (specifically GPT-4.1 mini) 

After successfully setting up our environment with MCP and providing a valid OpenAI API key, we’ll use a certain popular note-taking application (Windows 10’s non-UWP notepad.exe). In it, we can find a moderately sized function that operates on arrays. We’ve moved the C-like output from Ghidra to VS Code and formatted it a bit for better readability.

We’ll again ask the model to decompile and identify the function, then rename it to describe the function’s purpose. Here’s the result:

Lastly, we’ll ask the AI agent to give descriptive names to all remaining unnamed parameters and variables in insertOrUpdatePairInArray. If this function call leads to calling other functions, the agent will rename them as well. Skipping the overly verbose output of the agent, let’s take a look at what it did with the function.

As you can see, the tools we used for decompiling Article.exe and notepad.exe have produced quite successful results.

Note that our current setup:

• Only works on one decompiled binary at a time
• Doesn’t allow for automation, as the used MCP client’s capabilities are quite limited 

We’ll try to address these two points in the next section.

Practical example 3: Using PyGhidra-MCP for static reverse engineering 

For this example, we’ll use a single AI agent interacting with Ghidra through PyGhidra’s MCP client. While the setup can support multiple agents and more complex workflows, we’ll keep it simple to focus on the core analysis steps and show how AI can assist in reverse engineering.

Here’s a list of what we’ll be using:

• Ghidra 11.3.2
• PyGhidra
• fastagent
• OpenAI’s GPT models (specifically, GPT-4.1 mini)

As our test subject, we’ll use a security-relevant Windows mechanism: Volume Shadow Copy Service (VSS). This component is frequently studied in cybersecurity research because it is only partially documented and often targeted by ransomware.

First, we’ll go over the configuration, since at the time of writing this article, combining fast-agent with PyGhidra has not yet been documented by the authors of either of these frameworks.

Here are the step-by-step instructions:

1. Install the uv package manager 
2. Create and activate a virtual environment: uv venv 
3. Install the fast-agent MCP package: uv pip install fast-agent-mcp 
4. Complete the setup by initializing fast-agent: uv run fast-agent setup

This sequence will create a simple project with default configuration files. 

In fastagent.config.yaml, we’ll modify the following fields:

After that, we’ll put our OpenAI API key in fastagent.secrets.yaml. Then, we need to define our agent in agent.py:

We can now launch the agent using uv run agent.py, which opens an interactive shell instance.

Let’’ begin by sending the following prompt to our assistant: 

• Using the provided tools, explain how vssadmin.exe can create shadow copies. If you encounter some imports, please import the required binary from C:\Windows\System32\. If the analysis of some binary is not yet complete, try to do other things in the meantime and only stop to wait if you have nothing else to do.

Although it may seem unexpected, vssadmin.exe can create shadow copies. This functionality is documented mainly for Windows Server editions, which may suggest it’s unavailable on Windows 10/11. In practice, the capability still exists and can be triggered under certain conditions (you can learn more about it here).

Here is the AI assistant’s response:

We should verify the agent’s output. We’ll check if all listed interfaces exist. They do, and they are well documented — you can find relevant Microsoft documentation pages below:

• CreateVssBackupComponentsInternal function (vsbackup.h) 
• IVssBackupComponents::InitializeForBackup method (vsbackup.h) 
• IVssBackupComponents::GatherWriterMetadata method (vsbackup.h) 
• IVssBackupComponents::FreeWriterMetadata method (vsbackup.h) 
• IVssBackupComponents::GatherWriterStatus method (vsbackup.h) 

As you can see, PyGhidra can easily follow the imports in binaries and then queue them for analysis in Ghidra.

Now, we’ll ask the agent to import all binaries with the prefix “vss” from System32, along with the volsnap.sys driver, which is part of the system shadow copy provider component. 

To test whether the agent will “cheat” or hallucinate, we won’t tell it about swprv.dll — the default shadow copy provider that works with volsnap.sys but has no direct references in the coordinator’s logic since it’s a hidden COM service.

In some cases, the agent can even “pretend” to use the tools and hallucinate their output, so we should also be aware of that.

Let’s proceed by sending these prompts:

• We’re going to be analyzing Windows shadow copies. Use the provided tools to import any binaries from C:\Windows\System32\ that have the prefix “vss” in their names. Some binaries have already been included in our previous conversations. 

• Import “C:\Windows\System32\drivers\volsnap.sys”

Now that we’ve imported everything we wanted, we’ll ask the agent to do the following:

• Using the provided tools and binaries, explain the logic flow for creating volume shadow copies from vssadmin.exe to volsnap.sys.

The described flow is generally logical and accurate, but as we mentioned previously, we wanted to see if the agent would be able to pick up a certain detail — how requests from VSSVC.exe go to volsnap.sys through swprv.dll. 

Some IOCTL codes only check the state of shadow copies, while the ones that actually modify them are in swprv.dll. Let’s give our AI agent a hint to help it find them.

• Explore the IOCTLs defined in volsnap.sys.

The agent identified two IOCTLs that are commonly targeted by ransomware, which is useful from a security research perspective. It also correctly did not find any IOCTLs related to snapshot creation, since no such logic exists in the code.

At the same time, the agent made a mistake: Even though every driver has a DriverEntry routine, it failed to identify them. This error isn’t critical for our analysis, but it shows that AI output still needs to be reviewed by an experienced reverse engineer.

To find out whether our agent can autonomously search the web, let’s try asking it some final questions:

• How does VSSVC.exe forward a request to create a shadow copy to volsnap.sys?

Looks like a miss, since the IOCTL in question represents the query “Is the volume currently offline?” As mentioned previously, this kind of hallucination can occur in cases where the model simply lacks the necessary information to proceed. 

Let’s now steer the model towards snapshot deletion a bit more, since this is generally the most interesting feature of VSS for security researchers:

• IOCTL 0x560010 is used for checking whether a volume is offline, so that’s not it. Check where IOCTL_VOLSNAP_DELETE_SNAPSHOT from volsnap.sys is used in the VSS coordinator VSSVC. If it’s nowhere to be found, then that means it’s used in a VSS provider, in which case, make sure to find out what binary represents it.

Now that the assistant itself has asked us for permission to check what’s missing, let’s grant it.

• Go ahead and check what other binaries might be missing. Use any source you deem necessary.
  

Bingo! Underlined in yellow is our last piece of the puzzle, swprv.dll.

This way, we were able to have the AI assistant explain the whole flow of the Windows shadow copy functionality. Allowing the agent to work on several binaries at once saved time by automating repetitive analysis tasks across multiple files.

The examples above demonstrate relatively successful cases of AI-assisted reverse engineering, but results are not always this straightforward. In the next section, we’ll look at common challenges engineers may face when working with AI agents and share practical ways to address them based on our experience.

Challenges and limitations of AI-assisted reverse engineering

While AI can help automate some parts of reverse engineering, it is not a silver bullet. Like any emerging technology, AI-assisted reversing comes with practical limitations and risks that your engineers must account for.

Hallucinations and the need for verification

AI models can produce confident but incorrect explanations, especially when analyzing complex or heavily obfuscated code. Decompiled output is often ambiguous, and models may infer intent that is not actually present.

This risk increases as obfuscation techniques evolve. Modern obfuscation can produce decompiled code that looks structurally valid but does not accurately reflect the program’s true behavior. In such cases, an AI model may reason about misleading constructs and draw conclusions from code that is intentionally deceptive.

For this reason, we recommend treating all AI-generated insights as hypotheses, not facts, and verifying them with experienced engineers through manual analysis and testing.

This limitation becomes more apparent as task complexity increases, since large, multi-step requests have a much higher failure rate and may produce partial or inconsistent results.

Apriorit’s solution: Make your prompts simple and focused. For example, you can ask the AI tool to identify the purpose of a single function instead of asking it to rename all functions at once.

Limitations with context awareness

Understanding a binary often requires broad context — such as how components interact, what role the software plays in a larger system, or what business or security goals drive certain design decisions.

AI models typically lack this project-level and business-level context. Even when decompiled code is accurate, the model may miss the bigger picture or misinterpret why certain logic exists, especially in large systems with layered architectures or domain-specific constraints.

Apriorit’s solution: Avoid using AI on very complex systems or tasks that require deep context awareness. Instead, reserve AI for smaller, well-scoped functions or patterns, and rely on experienced engineers for system-level analysis.

Privacy and data exposure risks

A critical consideration when using AI in reverse engineering is data privacy, particularly when you’re using cloud-hosted models. Decompiled binaries may contain proprietary logic, sensitive intellectual property, or confidential data that you wouldn’t want anyone to see. 

Uploading such material to third-party services without authorization can violate legal, contractual, or ethical obligations.

Apriorit’s solution: When working with sensitive material, we strongly recommend using local or self-hosted AI models, or limiting AI use to non-confidential samples and controlled research environments.

At Apriorit, we never submit your code, binaries, or decompiler output to external AI services without prior approval. We also make sure you are comfortable with AI being used at all before it becomes part of our reverse engineering process. Any use of AI is discussed up front and handled carefully, so there are no surprises and your sensitive data stays protected.

Michael Teslia, Program Manager at Apriorit

Tooling maturity and reliability

Frameworks such as MCP are still in relatively early stages of development. AI agents may fail to complete tasks due to timeouts, tool incompatibilities, or unclear prompt-to-tool mappings. In some cases, a tool’s capabilities may not cover all elements of a binary, so certain tasks might be skipped or remain incomplete.

More advanced models like GPT-4 or Claude generally perform better on reverse engineering tasks, but they can still fail unpredictably when handling large or complex requests.

Apriorit’s solution: To avoid issues with your tools, we recommend using them separately. Instead of asking a single tool to do many dependent steps at once — like decompiling a bunch of functions, renaming them, and analyzing logic in a single request — it’s usually better to guide it step by step. This makes it easier to spot failures and prevent small mistakes from snowballing.

How Apriorit can help you reverse engineer your software (with or without AI)

Apriorit’s reverse engineering team has over 20 years of experience helping businesses analyze, optimize, and secure software. We design complex systems, from legacy applications and proprietary protocols to obfuscated and security-critical software.

Working with Apriorit, you get:

• A full range of software reverse engineering services. We deliver end-to-end reversing, including binary and firmware analysis, protocol reversing, source code recovery, and security assessments.
• Expert consulting on AI adoption for reverse engineering. We help you evaluate where AI makes sense in your software reverse engineering project, which tools to use, and where manual analysis is the safer or more effective choice.
• Smart and secure AI integration into SRE workflows. Our team knows how to apply AI efficiently, validate its output, and integrate it safely into existing reversing processes without compromising data or results.
• Clear results you can act on. You receive detailed reports, insights, and recommendations tailored to your project goals.

At Apriorit, we use AI only when it adds real value and only with your approval. We never share sensitive data externally without your consent, and we perform reverse engineering strictly within legal and ethical boundaries.

Apriorit helps CTOs and engineering leaders safely bring AI into their reverse engineering workflows. Our 20+ years of legal reverse engineering experience combined with AI expertise allows us to modernize and secure complex systems faster, without risking IP or reliability.

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