Forensic Analysis of the HackRF One with PortaPack Mayhem Firmware

Introduction to the show: ident: Security Radio. Generated commentary on the latest security and cryptography papers.

Nadia: Today's paper: "Forensic Analysis of the HackRF One with PortaPack Mayhem Firmware".

Elias: The gist The study establishes, at source-code level, where forensically relevant data reside on the microSD card of a device running Mayhem v2.0.1, in which format,

Nadia: First, who's behind it and why it matters.

Title and authors: Nadia: They suggest that practitioners should take the advice from the study and remove the microSD card before you switch your device on, imaging it as a whole disk instead of just targeting a single partition.

Elias: That makes sense because it helps ensure you capture all potential traces without missing anything that might be hidden outside the main user data areas.

Priya: I agree, getting a full disk image is always better than trying to isolate just one partition, especially when dealing with complex file systems like FAT32.

Nadia: Then they push for keeping the hash report of your acquisition tool together with the image itself as proof that the image hasn't changed since you performed the acquisition.

Elias: That’s basic digital forensics practice, but it reinforces the need to document those initial integrity checks explicitly in this context.

Priya: It ties back to what we talked about earlier—we need verifiable proof that our analysis is sound, not just a collection of interesting findings on one specific device.

Nadia: They also recommend being careful with the scope; they state their rules are limited specifically to Mayhem v2 point 0.one and one SD card for this particular study <ref:2610.20647#pg1>.

Elias: Which is a necessary constraint, because generalizing that analysis across different firmware versions or hardware setups would be way too broad without further testing.

Priya: That limitation is important—it tells us this specific methodology is tuned to that exact version, so we have to be careful when applying it elsewhere.

Nadia: They also mention future work involving differential analysis using controlled sets of actions on the same device to see how those artifacts change.

Elias: That suggests they aren't just looking at static files, but dynamic changes based on user interaction, which opens up a new direction for investigation.

Priya: I’m curious if that dynamic approach can help us track specific user behaviors or application usage more accurately than the current static catalog.

Nadia: That’s the potential—moving from just knowing *what* files exist to understanding *how* those files were modified over time.

The paper's summary: Elias: Basically, they’ve given us a detailed way to find, interpret, and recover usage traces on these specific devices based on source code analysis.

Nadia: They established that you can find an artifact catalogue where every piece of data is linked to the source code file that writes it.

Priya: And they showed how you can reconstruct user activity by interpreting timestamps according to system-specific rules, considering things like the RTC battery state.

Elias: Plus, they provide a method for cluster-level recovery of deleted in-phase/quadrature IQ captures and signal decoding validated against tools like rtl four hundred thirty-three.

Priya: It provides a concrete path for examiners to find and interpret usage traces on SDR hardware with this specific firmware.

Nadia: This work is valuable because it offers an examiner a way to find, interpret, and recover usage traces based on source code and verifiable methods.

Elias: It’s a very focused study that provides the necessary detail for anyone needing to analyze these systems running Mayhem v2 point 0.one <ref:2610.20647#pg1,running Mayhem v2.0.1>.

Priya: It’s a solid piece of research that gives us actionable steps for analyzing this specific type of embedded device forensics.

The paper's improvements: Nadia: So we looked at how they found the data on that Mayhem firmware, and now they’re talking about how to make that analysis better in practice, like adding more detail to the catalogue.

Elias: Yeah, they suggest linking every single artifact back to the source code file that actually wrote it. It makes tracing things way clearer because you know exactly which function is responsible for creating that file.

Priya: That mapping is useful because it connects the abstract data on an SD card to the actual logic inside the program, which helps us trust what we’re seeing, right?

Nadia: Exactly. It moves it from just finding a file to understanding *why* that file exists and what process created it.

Elias: And they also talk about dynamic timeline interpretation. They suggest using rules based on how the real-time clock battery is acting, because that affects how the system records time stamps on the files.

Priya: That’s important because if you don't account for the RTC battery state, your whole timeline reconstruction is just a guess based on one specific assumption.

Nadia: It forces us to be precise about those assumptions. They say you have to figure out how fast the universe is expanding near us, and that affects time readings in some systems, so it’s not just UTC.

Elias: And they mention inferring time zones based on which system wrote the file, like Windows versus Linux rules being different. That’s a big caveat for anyone trying to build a universal timeline for these devices.

Priya: It really shows that even with source code mapping, the measurement itself is tricky because the underlying operating system is doing things differently.

Nadia: They also give this workflow for recovering deleted data, focusing on cluster-level recovery of those IQ captures from unallocated space.

Elias: They specifically point out how to handle trimmed files when IQ Trim runs. When that happens, they write the temporary data to a minus tmp file, and then the original gets deleted.

Priya: So if you’re looking for evidence of an old capture that someone tried to hide by trimming it, this method tells you exactly where to look for that temporary evidence.

Nadia: It’s about making sure we don't miss those remnants of deleted data when we scan the whole disk image.

Elias: These improvements suggest a way to build a more reliable forensic toolkit for SDR devices, moving past just basic file listing.

Priya: It helps bridge the gap between finding raw data and actually proving what that data means in terms of user activity and signal quality.

Nadia: So, the next thing we have to consider is how this all fits into a bigger picture, maybe comparing it to those other papers on watermarks or vulnerability benchmarking.

Conclusion: Nadia: So to wrap up, this paper on "Forensic Analysis of the HackRF One with PortaPack Mayhem Firmware" shows how you can actually get into those device traces using source code knowledge and specific workflows.

Elias: Yeah, it gives us a real practical method for finding deleted IQ captures and decoding signals by cross-checking them against independent tools like rtl four hundred thirty-three.

Priya: It’s solid because it doesn't just claim to find data; it shows you how the specific format dictates the recovery path, which helps us judge if the results are trustworthy.

Nadia: It really changes how we think about analyzing embedded hardware that isn't designed with forensic logging in mind.

Elias: Exactly. It moves us away from just looking at file signatures and into understanding the actual execution flow of the operating system, which is where a lot of the hidden usage traces live.

Priya: And for someone just listening, it means that even if you have this type of SDR device in your hands, there’s a specific set of rules you need to know to actually extract anything useful.

Nadia: That's the point. It's not some massive new technology; it's about applying deep knowledge of the code to make existing hardware yield actionable data for investigation.

Elias: The study is limited, obviously, only to Mayhem v2 point zero.one and one specific SD card setup, but that’s fine because it sets a clear baseline for what we *can* do with this approach right now.

Priya: And the authors themselves flag that the method relies heavily on knowing where the data is written in the code, so if you don't have access to that source, you can't do this kind of analysis.

Nadia: So we’ve seen how they map artifacts to source code, how they handle time zone rules based on battery status, and how they recover trimmed data segments.

Elias: It’s a useful guide for anyone working in embedded forensics who needs to know exactly where the relevant data resides when you're dealing with these types of SDR platforms.

Priya: It helps ground the theory in what’s actually measurable—the PSD and envelope analysis they use to classify signals is a solid way to prove signal content.

Nadia: Yeah, that’s what matters. We need those verifiable methods so we can defend our findings in practice when we're dealing with real-world evidence from these kinds of devices.

Elias: It’s a good piece of work because it provides a repeatable process for analyzing this specific device family under Mayhem v2 point zero.one conditions.

Priya: So, the big picture here is that we can use code analysis to unlock forensic potential in hardware that often gets ignored by traditional digital forensics tools.

Nadia: Exactly. This leads us perfectly into looking at those other papers on watermarking and benchmarking, because we need to know how reliable these new methods are when applied broadly.