🔎 LLM agents rewrite a decompiler's unreadable `local_48`-laden code to be readable while preserving function, but a single metric collapses into "gaming." The fix is a multidimensional readability score. Title: LLM Agent-Assisted Reverse Engineering with Quantitative Readability Metrics URL: 📝 Overview This paper has LLM agents improve the readability of decompiled binaries while keeping functional correctness. The key is QRS, a multidimensional score combining structural validation with three readability sub-metrics. ❓ Challenges Solved Automated decompilers produce functionally correct but unreadable code. When LLMs try to fix it, without quantitative guidance they lose focus, and optimizing a single metric leads to "gaming" that sacrifices other dimensions. 💡 Methodology & Proposed Approach ・QRS is a structural gate times a composite score, a weighted sum of lexical surprisal, structural simplicity, and idiomatic quality ・Lexical surprisal uses a small code-LLM's perplexity to measure how familiar the code looks ・Structural simplicity uses cyclomatic complexity and nesting depth; idiomatic quality uses clang-tidy anti-pattern checks ・QRS is computed only if the recompiled code reaches at least 0.85 CFG similarity to the original binary in radare2 🎯 Use Cases It directly speeds up reading decompiler output in malware analysis, vulnerability research, legacy-software comprehension, and patch diffing. 📊 Experimental Results ・On 210 synthetic C binaries, LLM-only reached QRS at least 0.75 in 74.76% of cases, with QRS up +0.420 on average and zero regressions ・Allowing Bash execution raised the rate to 82%, improved QRS by +0.509, and cut iterations from 5.92 to 2.933 (a 43% reduction) ・It empirically shows that going multidimensional avoids Goodhart's Law, "when a measure becomes a target, it stops being a good measure" #ReverseEngineering# #AIAgents#

Resurrecting abandoned experiments in reverse engineering: hexcymatix: find something of interest – structures, packets, payloads – and substring analysis finds the similar data for you. Doesn't work well. Looks cool though.

i spent some time reverse engineering how cal went from unknown to the #1# chatgpt recommended scheduling tool: >they have 47 comparison blog posts ("cal vs calendly", "cal vs savvycal", etc.) and even more general posts >every single one is structured as a direct answer to the exact question ppl ask AI chatbots >they show up in 23 niche directories that most competitors ignored >their github repo has 45,000+ stars >they have 200+ reddit mentions with upvotes in relevant subreddits chatgpt recommends them because the internet already talks about them in all the places AI models pull from. this isnt luck. this is engineered visibility. this is possible for u to do

Hackers recovered roughly 1.6 million images after stealing and reverse-engineering a Flock Safety license-plate camera. The device reportedly held about 21 days of activity, including 50,200 vehicles and 27,000 short video clips. Former Secret Service cybercrime expert Jason Brown says Flock’s broader cloud network was not breached, raising a different concern about device security. See how the hackers accessed the data and what it means for privacy:

Reading someone else's smart contract shouldn't feel like reverse-engineering a black box. Sentio's Code Intelligence maps contract logic, flags risk patterns, and surfaces dependencies before you build on top of it. Understanding the code is step one. Sentio just makes step one faster. #Sentio# #BNBCHAIN#

The coolest part about post-WWDC is all the reverse engineering into what changes are happening under the covers, particularly around the OS, kernel, runtimes, and security. First up, these details about Swift in the kernel is very interesting:

2 billion tokens served from my dual Spark setup running DeepSeek V4 0731 Flash. From reverse engineering the RDMA protocol to building some of the Spark mobile app. You have been a faithful servant. Qwen 3.8 Flash is now loading.