Unlocking Bug Bounty Success: Expert Tips from Dan McInerney

Jan 27, 2025 By Dan McInerney

What’s the secret sauce behind consistent bug bounty success? Well, the answer lies in a strategic approach: dissecting a single project, identifying hot spots, leveraging the right tools, and focusing on impactful vulnerabilities. Pair this with huntr’s newest target, Model File Vulnerabilities (MFVs), and you have a winning formula for diving into a lesser-explored, high-impact attack surface: AI/ML systems and their model file formats.

 

 

Start Small, Go Deep

My first piece of advice: focus on a single project and understand it deeply. While it's tempting to cast a wide net, success often comes from narrowing your scope and mastering one target.

“Start with one project and get to know that project really, really well.”

 

Begin with web application vulnerabilities, particularly in areas where user inputs are handled—like file uploads and downloads. These are common hotspots for vulnerabilities that can lead to impactful discoveries like Local File Inclusion (LFI) or Arbitrary File Overwrite.

In the context of AI/ML systems, this principle applies directly to model file handling. Many AI tools load and process complex file formats like Pickle, ONNX, Safetensors, and GGUF, making them prime candidates for deeper investigation.

 

Map the Attack Surface with Tools

In my video above, I emphasize the importance of using tools strategically to identify potential vulnerabilities. Here’s how you can apply this advice:

  • Static Analysis: Tools like Snyk can highlight areas in the code where vulnerabilities might exist. While most flagged issues may not be exploitable, they guide you toward areas worth investigating, such as file parsing functions or memory allocation routines.

  • LLMs for Creative Ideas: Large Language Models (LLMs) can help you brainstorm bypass methods or alternative approaches to existing security mechanisms. For example, LLMs can suggest ways to exploit insufficient validation in a model loader.

  • Proxies and Interceptors: Tools like Burp Suite are invaluable for analyzing web applications. Extensions and automated scans can provide insights into vulnerable endpoints. 

 

Hunting Hot Spots in Codebases

Certain areas of code are more prone to vulnerabilities. I recommend treating the codebase like a heat map. For AI/ML systems, this includes:

  • File Handling: Improper validation during file uploads, downloads, and artifact processing can lead to severe vulnerabilities. My example with MLflow highlights how focusing on artifact operations uncovered a Local File Inclusion vulnerability.

  • Authentication Logic: Login flows and session management often house critical bugs that attackers can exploit for unauthorized access.

  • Memory Management: In low-level languages like C/C++, improper memory handling can lead to buffer overflows or heap corruption.

Huntr’s MFV Guide dives even deeper into file handling, encouraging researchers to explore vulnerabilities in model file formats. These bugs—like GGUF heap overflows or ONNX custom operator vulnerabilities—can result in high-severity exploits, including remote code execution.

 

Why MFVs Are the Future of Bug Hunting

Model File Vulnerabilities (MFVs) represent an emerging and largely uncharted territory in AI/ML security. While API and web vulnerabilities have been heavily researched, the way AI systems load and process model files is still a fresh attack surface.

Key MFV Examples

  1. GGUF Heap Overflow: A vulnerability in the GGUF format where improper header validation can lead to out-of-bounds writes on the heap. This highlights how seemingly simple parsing logic can expose critical bugs.

  2. Keras Lambda Layer RCE: Malicious Lambda layers in Keras models can execute arbitrary code upon loading, turning model files into powerful attack vectors.

  3. ONNX Custom Operator Vulnerabilities: By exploiting custom operator support or complex control flow, attackers can manipulate execution paths or achieve code execution.

“Memory corruption bugs in ML model loaders often occur where file parsing meets memory allocation.”

 

Getting Started with MFVs

Huntr makes it easy to dive into MFV hunting, even if you’re new to this space. Start small and build your skills with these steps:

  1. Search for File Parsing Functions: Review how model files are loaded, focusing on header parsing and memory allocation.

  2. Use Tools Effectively: Employ fuzzing tools like AFL++ with structure-aware fuzzing, debuggers like GDB, and sanitizers like ASAN to identify vulnerabilities.

  3. Explore Common Pitfalls: Look for integer overflows, unchecked array access, and blind trust in header values—patterns that frequently lead to exploits.

  4. Test Extreme Cases: Modify legitimate model files to test edge cases, such as maximum or zero values in headers, to uncover parsing issues.

 

Join the Hunt

With that, I think it's time to fire up your favorite tools, start dissecting codebases, and dive into the world of bug bounty hunting. Join the hunt at huntr.com and explore how our MFV Guide can help you uncover the next big vulnerability.

Dan McInerney