New nightMARE Python Library to Analyze Malware and Extract Intelligence Indicators

Since its public debut in October 2025, nightmare has quickly become a vital tool for malware analysts seeking to streamline static and dynamic analysis workflows.

Developed by Elastic Security Labs, nightmare brings together mature open-source reverse engineering components under a unified Python API.

Rather than forcing users to juggle disparate dependencies, nightmare leverages Rizin via rz-pipe for disassembly and the Unicorn engine for lightweight emulation.

This cohesive design empowers researchers to rapidly craft configuration extractors, carve IoCs, and automate recurring analysis tasks.

Emerging from a need to reduce code duplication across Elastic’s internal tooling, nightmare builds on practices honed over thousands of sample analyses.

Elastic analysts noted that many proprietary scripts suffered from fragile dependency chains and inconsistent abstractions.

By encapsulating common patterns—such as pattern matching, instruction emulation, and cross-reference enumeration—within a robust library, nightMARE provides a stable foundation for both seasoned and novice reverse engineers.

Upon installation, nightmare exposes three main modules: analysis, core, and malware. The analysis module integrates Rizin to enable disassembly, hex-pattern searches, and function enumeration.

The core module offers utilities for bitwise operations, regex-based extraction, and data casting.

Finally, the malware module groups family-specific extractors—ranging from Smokeloader to LUMMA—into versioned sub-packages that demonstrate real-world uses of the API.

Elastic researchers identified a significant spike in LUMMA stealer campaigns in mid-2025, underscoring the value of rapid configuration extraction.

Through nightmare’s emulation capabilities, analysts can instantiate a WindowsEmulator, register Import Address Table (IAT) hooks on APIs such as Sleep, and execute targeted code sequences in seconds.

By intercepting decryption routines in-process, nightMARE automates the recovery of C2 domains without manual unpacking or debugger-driven tracing.

Infection Mechanism and Emulation-Driven Extraction

nightMARE’s emulation framework offers a lightweight alternative to full-scale sandboxing. Consider the common technique where malware invokes Sleep before proceeding to C2 decryption.

The following code snippet demonstrates how nightMARE’s WindowsEmulator hooks Sleep in a LUMMA sample, capturing timing behavior and enabling uninterrupted emulation:-

import pathlib
from nightMARE.analysis import emulation

def sleephook(emu: emulation.WindowsEmulator, args):
    print(f"Sleep {emu.unicorn.reg_read(emulation.unicorn.x86_const.UC_X86_REG_ECX)} ms")
    emu.do_return()

def main():
    path = pathlib.Path(r"C:samplesDismHost.exe")
    emu = emulation.WindowsEmulator(is_32bits=False)
    emu.load_pe(path.read_bytes(), stack_size=0x10000)
    emu.enable_iat_hooking()
    emu.set_iat_hook(b"KERNEL32.dll!Sleep", sleephook)
    emu.unicorn.emu_start(0x140006404, 0x140006412)

By intercepting the Sleep call, the emulator advances past timing obfuscation and resumes execution at the next instruction.

Combined with emu.get_data() and emu.get_xrefs_from(), analysts reconstruct decryption key and nonce addresses, allocate memory buffers, and invoke the malware’s ChaCha20 routine directly.

Ultimately, nightMARE outputs a decrypted list of C2 domains, ready for threat intelligence ingestion.

With version 0.16, Elastic Security Labs continues to expand nightMARE’s repertoire, adding emulation support for additional API hooks, enhancing pattern-matching accuracy, and refining malware module templates.

As emerging threats exploit novel obfuscation and packing schemes, nightMARE stands poised to accelerate analysis pipelines and empower the community’s collective defense.

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