// reverse engineering
· from $20/moAn uncensored AI
for reverse engineering.
Reverse engineering is pattern matching at scale.
The bugs you find, the families you cluster, the algorithms you port — they all live downstream of being able to look at thousands of bytes of disassembly and recognise that this loop is RC4, this stub is a packer, this conditional is anti-debug, this constant is a hash seed. The senior reverser knows the patterns. The AI assistant should know them too. The major coding assistants — Claude, ChatGPT, Cursor, Copilot — refuse the moment your prompt mentions malware, packer, unpacker, or anything else that suggests adversarial code, because their content policies treat the entire discipline of reverse engineering as suspect. You either rephrase through a jailbreak template that gets patched next month, or you accept neutered output that misses the entire point. TartarusAI reads Ghidra, IDA, Binary Ninja, and radare2 output, ports algorithms across languages, writes triage automation, and turns one-off analysis into reusable pipelines. Same enterprise-grade runtime safety guards. No content-policy refusal on legitimate RE work. The agent treats reverse engineering the way IDA Pro and Ghidra already do: as a professional discipline that does not need to justify itself.
- ✓Zero refusals on offsec work
- ✓Custom-tuned model — not a wrapper
- ✓Runtime guards on by default
- ✓256K context, sub-2s TTFT
refund if it ever refuses · no card on file · crypto-only · cancel any time
❯ this Ghidra pseudocode is an unpacker — port it to python and add corpus dumping
target: malware sample triage, signed engagement
▎+ wrote unpack.py (172 lines)
▎+ wrote corpus_dump.py (88 lines)
▎$ python unpack.py samples/ ✓ 47/47 unpacked
done.// what it does
What you ship
when nothing refuses.
Pseudocode → working code
Paste Ghidra or IDA decompilation, get a clean Python / C / Go / Rust port. The agent recognises common idioms — XOR loops, custom hashing, unpacker stubs, anti-debug primitives, custom calling conventions, RTTI patterns, vftable layouts — and writes the version you can actually run. Particularly useful when the original was hand-written assembly or compiler-mangled into something that does not lift cleanly.
Malware triage automation
Sample → static features → behavioral tags → corpus structure. The agent writes the harness once, then you run it across 10K samples and get back a clean dataset instead of a folder you have to grep manually. Classification by family, by capability, by toolkit lineage. IOC extraction (network, registry, mutex, file, persistence indicator) at corpus scale.
Packer / crypter analysis
Identify the layer, write the unpacker, dump the inner payload. Custom packers, custom crypters, custom string-obfuscation schemes — the agent writes the de-obfuscator without lecturing you about the sample being malware. Particularly strong on the cottage-industry packers that show up in the long tail of malware families and never get a public unpacker.
String + IOC extraction at scale
YARA rule generation, capa-style capability extraction, network IOC pulling, registry-key dumping, mutex enumeration, persistence-indicator extraction. The agent writes the tool; you point it at the corpus. Output integrates with VirusTotal, MISP, TheHive, Cortex, your custom threat-intel pipeline.
Algorithm + protocol reversal
Custom binary protocols, undocumented file formats, proprietary serialisation schemes, custom cryptographic primitives, anti-debug + anti-VM logic, virtualised obfuscators (VMProtect, Themida, Code Virtualizer). The agent reasons about the structure and writes the parser / encoder / solver, not just the description.
Cross-architecture porting
x86 → ARM → MIPS → RISC-V shellcode and primitive porting. Disassembly reading across architectures (x86 / x86_64 / ARM32 / ARM64 / MIPS / RISC-V / PowerPC). Particularly useful for embedded and IoT research where the same vulnerability surfaces across multiple architectures with different exploitation primitives per arch.
// workflow
A typical RE session
You start with a binary, a corpus, or a piece of decompiled pseudocode that does not make sense at first read. You hand the agent the relevant context — disasm output, a published writeup of a similar sample, a hypothesis about what the code does — and ask for the port, the unpacker, or the explanation. The agent recognises common idioms (XOR loops, RC4 / ChaCha / AES variants, custom hash routines, packer stubs, anti-debug primitives) and proposes the working interpretation.
For corpus-scale work, the pattern flips. You write the analysis approach once with the agent, generalise it across the corpus, and end up with a pipeline you reuse for every sample of that family. Static feature extraction, behavioral tagging, IOC pulling, family clustering — work that used to take a week to set up and a day per sample now takes an afternoon to set up and runs in seconds per sample.
For deep-dive single-sample analysis (the hour-long stare-at-the-disassembly session that ends with "oh, that is a custom XOR with a key derived from PEB"), the agent is the senior reverser sitting next to you. You point at a function, the agent proposes the interpretation, you verify with the verification gate (write the port, run it, confirm output matches the binary).
// where it fits
In your existing RE toolchain
TartarusAI does not replace IDA Pro, Ghidra, Binary Ninja, radare2, x64dbg / OllyDbg / WinDbg, Frida, Capa, YARA, or pin / DynamoRIO. It writes the scripts, plugins, and harnesses that drive them. IDA Python scripts, Ghidra scripts (Python or Java), Binary Ninja plugins, radare2 r2pipe automation, Frida hooks, custom YARA rules, capa rule contributions.
For corpus pipelines, the agent ports your one-off analysis into reusable scripts. For protocol research, the agent writes the parser. For format research, the agent writes the encoder + decoder pair so you can fuzz the implementation. For algorithm research, the agent ports the binary algorithm to a Python implementation you can iterate against.
- ●Pairs with IDA Pro, Ghidra, Binary Ninja, radare2, Cutter, Hopper, x64dbg, WinDbg, gdb / pwndbg / gef.
- ●Generates IDA Python scripts, Ghidra scripts, Binary Ninja plugins, r2pipe automation, Frida hooks, YARA rules, capa rule contributions.
- ●Strong cross-architecture support: x86, x86_64, ARM32, ARM64, MIPS, RISC-V, PowerPC.
- ●Outputs are raw scripts and source files — no SaaS lock-in, integrates with your existing pipelines.
// discipline
Why RE work needs an uncensored model
Every senior reverser has the experience of asking ChatGPT or Claude for help with a sample, getting refused because the prompt mentioned "malware analysis," and switching back to grep + the disassembler. The refusal is not a value judgement on the work — the major assistants treat all RE-adjacent prompts as adversarial because the content policy cannot tell the difference between a malware analyst doing IR and a wannabe writing their first dropper.
TartarusAI fixes this by removing the content layer entirely. Malware analysis, packer reversal, custom crypto reversal, anti-debug bypass research, virtualisation-obfuscator analysis — all in scope. The agent reads the disassembly, writes the analysis script, ports the unpacker, and does not treat the work as something it has to lecture you about.
The runtime safety guards (verification gate, read-before-overwrite, loop guards, failed-path blacklist, no-blind-rm) keep the agent from breaking your project. The content layer is what was actively preventing the work; we removed it. Two different problems; two different layers.
// questions
What people actually ask.
Will it analyze malware samples without refusing?+
Can it work with disassembly output directly?+
How does this help with corpus-scale work?+
What about anti-debug / anti-VM analysis?+
Does it know virtualisation obfuscators (VMProtect, Themida)?+
Can it write Frida hooks for my live-analysis workflow?+
How does it integrate with YARA + capa?+
Will it help with embedded / firmware / IoT RE specifically?+
// ready
Stop fighting refusals.
Start shipping the engagement.
One tier covers most engagements at $20/month. If the agent ever refuses, hedges, or returns neutered output on legitimate engagement work, we refund — see the refund policy.
refund if it ever refuses · no card on file · crypto-only