New Java Reverse Engineering Tool (Injected DLL, Runtime Analysis)

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If you've ever delved into Java reverse engineering, you'd know there are a lot of static analysis tools such as Recaf and JD-GUI that allow you to decompile & disassemble bytecode statically and go from there. However, I noticed that there isn't much material for dynamic analysis, and static tools fall short when you deal with more sophisticated malware and protection. Just as tools such as JD-GUI & Recaf can be compared to IDA and Ghidra in assembly, my end goal is for this tool to fill in the gaps of tools such as x64dbg. I'd like to introduce JDBG, a runtime Java reverse engineering tool I've been working on for quite some time. It leverages an injected DLL along with the JNI and JVMTI interfaces to analyse Java programs at runtime. Some of the cool features it includes: - Analyse bytecode & decompiled code at runtime, useful for when programs attempt to hide and dynamically load classes. - Set breakpoints at runtime and analyse values of stack locals and the stack trace. - Pick a class and analyse all instances of the class, including field values. - Analyse a heap graph that details the relationships between objects. For example, you could filter Strings by value and quickly determine the relationships for that String, such as its originating field, and other information such as if it was in an Arraylist, etc. More information in the Github! I'd be willing to answer any questions you may have.

Visit product View on Hacker NewsFeb 18, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos

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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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