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Mohammad Waseem
Mohammad Waseem

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Mastering Email Flow Validation in Rust Without Documentation: A Security Researcher’s Approach

#rust #security #email

Introduction

Effective email flow validation is crucial in modern cybersecurity, especially when verifying the authenticity and integrity of incoming communications. For security researchers tackling this challenge, Rust offers a compelling combination of safety, performance, and concurrency. However, tackling this task without comprehensive documentation demands a strategic, code-first mindset.

In this blog, we’ll explore a methodology for validating email flows in Rust, emphasizing key techniques, practical code snippets, and best practices to handle the ambiguities often encountered in security-focused development.

Understanding the Challenge

Email validation involves multiple components: verifying sender authenticity, checking DKIM/DMARC/SPF records, examining message headers, and more. Without proper documentation, a researcher must reverse-engineer protocol behaviors, analyze network traffic, or interpret existing codebases.

The central goal is to build a robust validator that can process email headers and message data efficiently, conforming to RFC standards, while remaining resilient to malicious inputs.

Setting Up the Environment

To get started:

cargo new email_validator --bin
cd email_validator
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Install dependencies, focusing on crates related to networking, parsing, and cryptography:

# Cargo.toml
[dependencies]
reqwest = { version = "0.11", features = ["json"] }
serde = { version = "1.0", features = ["derive"] }
tokio = { version = "1", features = ["full"] }
mailparse = "0.10"
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Parsing Email Headers and Content

In absence of documentation, the first step is to programmatically parse raw email data. The mailparse crate is instrumental here.

use mailparse::*;

fn parse_email(raw_email: &str) -> Result<ParsedMail> {
    parse_mail(raw_email.as_bytes())
}

// Example usage with raw email data
let raw_email = "From: attacker@example.com\nTo: target@example.com\nSubject: suspicious activity\n\nPlease verify your account.";
match parse_email(raw_email) {
    Ok(parsed) => println!("Parsed headers: {:?}", parsed.headers),
    Err(e) => eprintln!("Error parsing email: {}", e),
}
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This step allows extraction of structured data from unstructured input, vital for further analysis.

Validating Sender Authentication: SPF, DKIM, DMARC

Without documentation, a researcher reverse-engineers the validation mechanisms from existing implementations or protocol specs.

  • SPF validation: Query DNS for the SPF record of the sender's domain. 
use reqwest::Client;

async fn fetch_spf_record(domain: &str) -> Result<String, reqwest::Error> {
    let client = Client::new();
    let url = format!("https://dns.google/resolve?name={}" , domain);
    let resp = client.get(&url).send().await?.json::<serde_json::Value>().await?;
    // Parse the DNS response to extract SPF record (simplified example)
    // Actual implementation requires understanding DNS response structure.
    Ok("v=spf1 include:_spf.google.com ~all".to_string())
}
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  • DKIM validation: Fetch DKIM public key from DNS and verify signature.
  • DMARC policy: Check policy records similarly.

Automated DNS queries combined with cryptographic signature verification (using crates like ring or rustls) are essential.

Handling Malicious Inputs and Edge Cases

Because of the security context, the validator must be resilient:

  • Implement strict input validation.
  • Avoid buffer overflows using Rust’s safety guarantees.
  • Employ regexes cautiously, focusing on RFC compliance.
  • Use asynchronous processing to avoid blocking.

Example: validating email addresses format

use regex::Regex;

fn validate_email_format(email: &str) -> bool {
    let email_regex = Regex::new(r"^[^@\s]+@[^@\s]+\.[^@\s]+$").unwrap();
    email_regex.is_match(email)
}
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Conclusion

Developing an email flow validator in Rust without proper documentation is feasible but requires a methodical approach: parsing raw data, reverse-engineering validation protocols, and employing Rust’s safety features. This disciplined process enhances security posture and improves understanding of email-based threats.

Security researchers must leverage Rust’s ecosystem and protocol standards to craft resilient, high-performance validators that can adapt to evolving threats and unexplored protocol behaviors.

Final Thoughts

Documentation gaps are common in security research. Building a layered understanding through code analysis, network traffic inspection, and protocol reverse-engineering is key. Rust’s type safety and concurrency model provide a strong foundation for resilient, efficient validators in this domain.

🛠️ QA Tip

Pro Tip: Use TempoMail USA for generating disposable test accounts.

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