reverse-engineering-rust-malware▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Reverse engineer Rust-compiled malware using IDA Pro and Ghidra with techniques for handling non-null-terminated strings, crate dependency extraction, and Rust-specific control flow analysis.
| name | reverse-engineering-rust-malware |
| description | Reverse engineer Rust-compiled malware using IDA Pro and Ghidra with techniques for handling non-null-terminated strings, crate dependency extraction, and Rust-specific control flow analysis. |
| domain | cybersecurity |
| subdomain | malware-analysis |
| tags | - rust - reverse-engineering - malware-analysis - ghidra - ida-pro - binary-analysis - rust-malware |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - DE.AE-02 - RS.AN-03 - ID.RA-01 - DE.CM-01 |
Reverse Engineering Rust Malware
Overview
Rust has become increasingly popular for malware development due to its cross-compilation, memory safety guarantees, and the complexity it introduces for reverse engineers. Rust binaries contain the entire standard library statically linked, producing large binaries with extensive boilerplate code. Key challenges include non-null-terminated strings (Rust uses fat pointers with pointer+length), monomorphization generating duplicated generic code, complex error handling (Result/Option unwrap chains), and unfamiliar calling conventions. Decompiling Rust to C produces unhelpful output compared to C/C++ binaries. Tools like Ghidra scripts for crate extraction, and training focused on Rust-specific patterns (2024-2025) help address these challenges. Notable Rust malware includes BlackCat/ALPHV ransomware, Hive ransomware variants, and Buer Loader.
When to Use
- When performing authorized security testing that involves reverse engineering rust malware
- When analyzing malware samples or attack artifacts in a controlled environment
- When conducting red team exercises or penetration testing engagements
- When building detection capabilities based on offensive technique understanding
Prerequisites
- IDA Pro 8.0+ or Ghidra 11.0+
- Rust toolchain for reference compilation
- Python 3.9+ for helper scripts
- Understanding of Rust memory model (ownership, borrowing)
- Familiarity with Rust string types (String, &str, CString)
Workflow
Step 1: Identify and Parse Rust Binary Metadata
#!/usr/bin/env python3
"""Analyze Rust malware binary metadata and extract crate dependencies."""
import re
import sys
import json
def identify_rust_binary(data):
"""Check if binary is Rust-compiled and extract version info."""
indicators = {
"rust_panic_strings": bool(re.search(rb'panicked at', data)),
"rust_unwrap": bool(re.search(rb'called.*unwrap.*on.*None', data)),
"core_panic": bool(re.search(rb'core::panicking', data)),
"std_rt": bool(re.search(rb'std::rt::lang_start', data)),
"cargo_path": bool(re.search(rb'\.cargo[/\\]registry', data)),
"rustc_version": None,
}
version = re.search(rb'rustc\s+(\d+\.\d+\.\d+)', data)
if version:
indicators["rustc_version"] = version.group(1).decode()
is_rust = sum(1 for v in indicators.values() if v) >= 2
return is_rust, indicators
def extract_crates(data):
"""Extract Rust crate (dependency) names from binary strings."""
crate_pattern = re.compile(
rb'(?:crates\.io-[a-f0-9]+/|\.cargo/registry/src/[^/]+/)'
rb'([\w-]+)-(\d+\.\d+\.\d+)'
)
crates = {}
for match in crate_pattern.finditer(data):
name = match.group(1).decode()
version = match.group(2).decode()
crates[name] = version
# Also check for common malware-relevant crates
suspicious_crates = {
"reqwest": "HTTP client",
"hyper": "HTTP library",
"tokio": "Async runtime",
"aes": "AES encryption",
"chacha20": "ChaCha20 encryption",
"rsa": "RSA encryption",
"ring": "Crypto library",
"base64": "Base64 encoding",
"winapi": "Windows API bindings",
"winreg": "Registry access",
"sysinfo": "System information",
"screenshots": "Screen capture",
"clipboard": "Clipboard access",
"keylogger": "Key logging",
}
capabilities = []
for crate_name, description in suspicious_crates.items():
if crate_name in crates:
capabilities.append({
"crate": crate_name,
"version": crates[crate_name],
"capability": description,
})
return crates, capabilities
def extract_rust_strings(data):
"""Extract strings handling Rust's non-null-terminated format."""
# Rust strings are stored as pointer+length, but string literals
# are often in .rodata as contiguous sequences
strings = []
ascii_pattern = re.compile(rb'[\x20-\x7e]{8,500}')
for match in ascii_pattern.finditer(data):
s = match.group().decode('ascii')
# Filter for malware-relevant strings
keywords = ['http', 'socket', 'encrypt', 'decrypt', 'shell',
'exec', 'cmd', 'upload', 'download', 'persist',
'registry', 'mutex', 'pipe', 'inject']
if any(kw in s.lower() for kw in keywords):
strings.append(s)
return strings
if __name__ == "__main__":
if len(sys.argv) < 2:
print(f"Usage: {sys.argv[0]} <rust_binary>")
sys.exit(1)
with open(sys.argv[1], 'rb') as f:
data = f.read()
is_rust, indicators = identify_rust_binary(data)
print(f"[{'+'if is_rust else '-'}] Rust binary: {is_rust}")
print(json.dumps(indicators, indent=2, default=str))
crates, capabilities = extract_crates(data)
print(f"\n[+] Crates ({len(crates)}):")
for name, ver in sorted(crates.items()):
print(f" {name} v{ver}")
if capabilities:
print(f"\n[!] Suspicious capabilities:")
for cap in capabilities:
print(f" {cap['crate']} -> {cap['capability']}")
strings = extract_rust_strings(data)
if strings:
print(f"\n[+] Suspicious strings ({len(strings)}):")
for s in strings[:20]:
print(f" {s}")
Validation Criteria
- Binary correctly identified as Rust-compiled with version info
- Crate dependencies extracted revealing malware capabilities
- Rust-specific string extraction handles fat pointer format
- Main entry point and core logic functions identified
- Encryption, networking, and persistence code located
References
How to use reverse-engineering-rust-malware on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add reverse-engineering-rust-malware
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches reverse-engineering-rust-malware from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate reverse-engineering-rust-malware. Access the skill through slash commands (e.g., /reverse-engineering-rust-malware) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★58 reviews- ★★★★★Pratham Ware· Dec 28, 2024
Registry listing for reverse-engineering-rust-malware matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Daniel Smith· Dec 28, 2024
Useful defaults in reverse-engineering-rust-malware — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Maya Wang· Dec 24, 2024
reverse-engineering-rust-malware has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Maya Li· Dec 8, 2024
reverse-engineering-rust-malware is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chaitanya Patil· Dec 4, 2024
reverse-engineering-rust-malware has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ishan Reddy· Dec 4, 2024
Useful defaults in reverse-engineering-rust-malware — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Alexander Okafor· Nov 27, 2024
reverse-engineering-rust-malware reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Piyush G· Nov 23, 2024
Solid pick for teams standardizing on skills: reverse-engineering-rust-malware is focused, and the summary matches what you get after install.
- ★★★★★Ishan Singh· Nov 23, 2024
I recommend reverse-engineering-rust-malware for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Amelia Huang· Nov 19, 2024
I recommend reverse-engineering-rust-malware for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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