Project 14: BYOVD Risk Assessment

Assess risk from vulnerable signed drivers in your environment.

Quick Reference

Attribute Value
Difficulty Level 2
Time Estimate 1-2 weeks
Main Programming Language PowerShell (Alternatives: Python, Bash)
Alternative Programming Languages Python, Bash
Coolness Level Level 3
Business Potential Level 2
Prerequisites OS internals basics, CLI usage, logging familiarity
Key Topics BYOVD, risk scoring

1. Learning Objectives

By completing this project, you will:

  1. Build a repeatable workflow for byovd risk assessment.
  2. Generate reports with deterministic outputs.
  3. Translate findings into actionable recommendations.

2. All Theory Needed (Per-Concept Breakdown)

BYOVD (Bring Your Own Vulnerable Driver) Risk

Fundamentals BYOVD refers to attackers loading legitimate, signed drivers that contain vulnerabilities to gain kernel-level privileges. Because the driver is signed, it can bypass code signing enforcement. The vulnerability then provides arbitrary kernel read/write, which can be used to disable security tools or hide rootkit components. Defensive work must therefore treat driver signing as a baseline, not a guarantee. BYOVD risk assessment inventories drivers, maps them to known vulnerabilities, and prioritizes remediation.

Deep Dive into the concept Driver signing policies solve one problem: untrusted origin. BYOVD exploits a different weakness: trusted origin but insecure implementation. Many hardware vendors ship drivers with unsafe IOCTLs that permit arbitrary memory access. Attackers can load these drivers to perform kernel patching or to disable protections. Because these drivers are signed and often whitelisted by default, they are a common rootkit enabler.

A BYOVD assessment starts with inventory. You need to enumerate loaded drivers and their file hashes, versions, and vendor metadata. Then you compare that inventory to vulnerability sources: vendor advisories, CVE databases, and official blocklists (like Microsoft’s vulnerable driver blocklist). The key is to map drivers to known exploits, not just to signatures. A driver may be signed but vulnerable on a specific version.

Risk scoring should consider three dimensions: exploitability, exposure, and impact. Exploitability reflects how easy it is to trigger the vulnerability. Exposure reflects how widely the driver is deployed or how easily an attacker can load it. Impact reflects the privileges gained. A high-risk driver is one that is easy to exploit, common in the environment, and yields kernel-level write access.

Remediation options include updating the driver, blocking it via policy, or removing it if not required. For Windows, Device Guard or HVCI can block known vulnerable drivers. For other OSes, you may need to blacklist modules or adjust kernel configuration. The assessment should produce a list of recommended actions with owners and timelines. BYOVD risk management is ongoing; new vulnerable drivers appear regularly, and updates may reintroduce risk if governance is weak.

How this fit on projects You will apply this in Section 3.2 (Functional Requirements), Section 7.1 (Frequent Mistakes), and Section 8.3 (Advanced Extensions). Also used in: P14-byovd-risk-assessment, P16-persistence-atlas.

Definitions & key terms

  • BYOVD: Using a signed but vulnerable driver to gain kernel privileges.
  • Blocklist: A list of known vulnerable drivers that should be blocked from loading.
  • IOCTL: I/O control interface used by drivers; vulnerable IOCTLs can expose kernel memory.
  • Risk score: A combined assessment of exploitability, exposure, and impact.

Mental model diagram

[Signed Driver] --(vulnerability)--> [Kernel Write]
        |
        v
[Rootkit Persistence / Defense Evasion]

How it works (step-by-step)

  1. Inventory drivers and collect hashes, versions, and vendors.
  2. Compare inventory to CVE and blocklist sources.
  3. Score drivers by exploitability, exposure, and impact.
  4. Recommend update, block, or removal actions.
  5. Track remediation to closure and re-assess periodically.

Minimal concrete example

driver, version, cve, risk
vuln.sys, 2.1.4, CVE-2022-XXXX, High
ok.sys, 3.0.1, none, Low

Common misconceptions

  • “Signed drivers are safe.” Signing only proves origin, not security.
  • “Blocklists are complete.” Blocklists are always incomplete and lag new CVEs.
  • “Only Windows has BYOVD.” Any OS with signed drivers can be abused.

Check-your-understanding questions

  • Why does BYOVD bypass code signing defenses?
  • What three factors make a driver high risk?
  • What remediation options exist besides removal?

Check-your-understanding answers

  • Because the driver is legitimately signed and allowed to load.
  • Exploitability, exposure, and impact.
  • Update to a patched version or block via policy/blacklist.

Real-world applications

  • EDR bypass techniques that rely on vulnerable signed drivers.
  • Enterprise risk assessments for driver inventories.

Where you’ll apply it You will apply this in Section 3.2 (Functional Requirements), Section 7.1 (Frequent Mistakes), and Section 8.3 (Advanced Extensions). Also used in: P14-byovd-risk-assessment, P16-persistence-atlas.

References

  • Microsoft vulnerable driver blocklist documentation
  • CVE databases and vendor advisories

Key insights Code signing blocks unknown drivers, but BYOVD abuses trusted ones.

Summary Inventory, map to vulnerabilities, score risk, and remediate vulnerable drivers.

Homework/Exercises to practice the concept

  • Find a public write-up of a BYOVD exploit and summarize the vulnerability.
  • Create a risk scoring template for driver inventory.

Solutions to the homework/exercises

  • Your summary should include driver name, vulnerability, and impact.
  • A good template includes exploitability, exposure, impact, and mitigation.

Kernel Code Signing, Module Integrity, and Trust Enforcement

Fundamentals Kernel code signing enforces that only trusted kernel components can execute. On Windows, Kernel-Mode Code Signing (KMCS) requires drivers to be signed; on Linux, module signing can be enforced by kernel configuration; on macOS, System Integrity Protection (SIP) and DriverKit reduce or eliminate third-party kernel extensions. These controls constrain rootkits by making it harder to load malicious kernel code, but they are not absolute because attackers can abuse vulnerable signed drivers or disable enforcement. For defenders, the critical task is to verify enforcement status, inventory loaded drivers/modules, and identify gaps.

Deep Dive into the concept Kernel code runs with the highest privileges, so OS vendors enforce signing to ensure provenance. Windows requires drivers to be signed by trusted authorities; boot-start drivers face stricter policies, and modern Windows can enforce HVCI (Hypervisor-enforced Code Integrity) to prevent unsigned or vulnerable drivers from loading. Linux provides module signing at build time, with enforcement controlled by kernel configuration (CONFIG_MODULE_SIG, CONFIG_MODULE_SIG_FORCE) and runtime parameters. macOS historically allowed kernel extensions (kexts), but SIP and the transition to System Extensions and DriverKit move third-party code out of the kernel.

The enforcement story is nuanced. A system may support signing but not enforce it, or it may enforce it only under specific boot configurations. On Linux, a kernel may be built with signing support but not forced; it will then accept unsigned modules but mark the kernel as tainted. On Windows, test signing or disabled enforcement weakens defenses. macOS SIP can be disabled in recovery mode, and older kexts may still load if allowed by policy. This means an audit must check both capability and policy: the configuration state that determines what is actually enforced.

For rootkit defense, audits must gather a full inventory of loaded modules/drivers and their signature status. You also need to detect driver downgrade risk and vulnerable-but-signed drivers (BYOVD). A driver that is signed is not necessarily safe. The audit should compare hashes and version information against blocklists and vulnerability databases. Enforcement should be validated by attempting a controlled load of an unsigned module in a lab. This creates evidence that policy is actually effective.

Finally, kernel integrity is not only about signatures but also about trust boundaries. Even with signing, an attacker might exploit a vulnerable signed driver to gain kernel write privileges. So the defender should combine signing audits with monitoring for driver loads, kernel taint flags, and configuration changes. The practical outcome is a policy: which drivers are allowed, how exceptions are handled, and what signals trigger “stop-the-world” responses.

How this fit on projects You will apply this in Section 3.2 (Functional Requirements), Section 4.2 (Key Components), and Section 6.1 (Test Categories). Also used in: P04-windows-driver-signing-audit, P05-linux-module-signing-audit, P06-macos-sip-system-extensions-audit, P07-bsd-securelevel-hardening, P14-byovd-risk-assessment, P15-kernel-event-monitoring-rules.

Definitions & key terms

  • KMCS: Windows kernel-mode code signing policy requiring trusted signatures.
  • Module signing: Linux mechanism to cryptographically sign and verify kernel modules.
  • SIP: System Integrity Protection on macOS that restricts kernel and system modifications.
  • Kernel taint: Kernel flag indicating loading of unsupported or unsigned modules.

Mental model diagram

[Signed Driver] --(verification)--> [Kernel]
[Unsigned Driver] --(blocked or taints kernel)--> [Kernel]
[Vulnerable Signed Driver] --(exploit)--> [Kernel Write]

How it works (step-by-step)

  1. Enumerate loaded drivers/modules and collect signature metadata.
  2. Check enforcement settings (policy flags, kernel config, SIP status).
  3. Compare driver hashes and versions to blocklists or vulnerability data.
  4. Record exceptions and validate with lab tests.
  5. Produce an audit report and remediation recommendations.

Minimal concrete example

driver, signer, status, hash
elam.sys, Microsoft, signed, 2d...
thirdparty.sys, Unknown, unsigned, 7a...
vuln.sys, VendorX, signed-but-blocklisted, 1f...

Common misconceptions

  • “Signed means safe.” Signed drivers can be vulnerable or malicious.
  • “Policy enabled means enforced.” Some systems allow test signing or disable checks.
  • “macOS does not have kernel risks.” Legacy kexts and SIP disablement still exist.

Check-your-understanding questions

  • What is the difference between signing support and signing enforcement?
  • Why is BYOVD still a risk when signatures are required?
  • What signals indicate that a kernel is tainted on Linux?

Check-your-understanding answers

  • Support means the OS can verify signatures; enforcement means it refuses to load unsigned code.
  • Attackers can use signed but vulnerable drivers to gain kernel access.
  • Kernel taint flags in /proc/sys/kernel/tainted indicate non-standard modules.

Real-world applications

  • Enterprise compliance audits for driver signing.
  • Hardening baselines for endpoint security.

Where you’ll apply it You will apply this in Section 3.2 (Functional Requirements), Section 4.2 (Key Components), and Section 6.1 (Test Categories). Also used in: P04-windows-driver-signing-audit, P05-linux-module-signing-audit, P06-macos-sip-system-extensions-audit, P07-bsd-securelevel-hardening, P14-byovd-risk-assessment, P15-kernel-event-monitoring-rules.

References

  • Microsoft documentation on KMCS and HVCI
  • Linux kernel module signing documentation
  • Apple documentation on SIP and System Extensions

Key insights Signing narrows the attack surface, but enforcement and audit determine real security.

Summary Audit enforcement, inventory drivers, and treat signed drivers as potential risk.

Homework/Exercises to practice the concept

  • List all loaded kernel drivers/modules and note signature status.
  • Find one driver that is signed but has had a CVE in the last 5 years.

Solutions to the homework/exercises

  • Your inventory should include name, path, signer, and status (signed/unsigned).
  • Any CVE-listed signed driver shows why signatures alone are insufficient.

3. Project Specification

3.1 What You Will Build

A tool or document that delivers: Assess risk from vulnerable signed drivers in your environment.

3.2 Functional Requirements

  1. Collect required system artifacts for the task.
  2. Normalize data and produce a report output.
  3. Provide a deterministic golden-path demo.
  4. Include explicit failure handling and exit codes.

3.3 Non-Functional Requirements

  • Performance: Complete within a typical maintenance window.
  • Reliability: Outputs must be deterministic and versioned.
  • Usability: Clear CLI output and documentation.

3.4 Example Usage / Output

$ ./P14-byovd-risk-assessment.ps1 --report
[ok] report generated

3.5 Data Formats / Schemas / Protocols

Report JSON schema with fields: timestamp, host, findings, severity, remediation.

3.6 Edge Cases

  • Missing permissions or insufficient privileges.
  • Tooling not installed (e.g., missing sysctl or OS query tools).
  • Empty data sets (no drivers/modules found).

3.7 Real World Outcome

A deterministic report output stored in a case directory with hashes.

3.7.1 How to Run (Copy/Paste)

./P14-byovd-risk-assessment.ps1 --out reports/P14-byovd-risk-assessment.json

3.7.2 Golden Path Demo (Deterministic)

  • Report file exists and includes findings with severity.

3.7.3 Failure Demo

$ ./P14-byovd-risk-assessment.ps1 --out /readonly/report.json
[error] cannot write report file
exit code: 2

Exit Codes:

  • 0 success
  • 2 output error

4. Solution Architecture

4.1 High-Level Design

[Collector] -> [Analyzer] -> [Report]

4.2 Key Components

Component Responsibility Key Decisions
Collector Collects raw artifacts Prefer OS-native tools
Analyzer Normalizes and scores findings Deterministic rules
Reporter Outputs report JSON + Markdown

4.3 Data Structures (No Full Code)

finding = { id, description, severity, evidence, remediation }

4.4 Algorithm Overview

Key Algorithm: Normalize and Score

  1. Collect artifacts.
  2. Normalize fields.
  3. Apply scoring rules.
  4. Output report.

Complexity Analysis:

  • Time: O(n) for n artifacts.
  • Space: O(n) for report.

5. Implementation Guide

5.1 Development Environment Setup

python3 -m venv .venv && source .venv/bin/activate
# install OS-specific tools as needed

5.2 Project Structure

project/
|-- src/
|   `-- main.py
|-- reports/
`-- README.md

5.3 The Core Question You’re Answering

“Which drivers create kernel-level risk even if signed?”

This project turns theory into a repeatable, auditable workflow.

5.4 Concepts You Must Understand First

  • Relevant OS security controls
  • Detection workflows
  • Evidence handling

5.5 Questions to Guide Your Design

  1. What data sources are trusted for this task?
  2. How will you normalize differences across OS versions?
  3. What is a high-confidence signal vs noise?

5.6 Thinking Exercise

Sketch a pipeline from data collection to report output.

5.7 The Interview Questions They’ll Ask

  1. What is the main trust boundary in this project?
  2. How do you validate findings?
  3. What would you automate in production?

5.8 Hints in Layers

Hint 1: Start with a small, deterministic dataset.

Hint 2: Normalize output fields early.

Hint 3: Add a failure path with clear exit codes.

5.9 Books That Will Help

Topic Book Chapter
Rootkit defense Practical Malware Analysis Rootkit chapters
OS internals Operating Systems: Three Easy Pieces Processes and files

5.10 Implementation Phases

Phase 1: Data Collection (3-4 days)

Goals: Collect raw artifacts reliably.

Tasks:

  1. Identify OS-native tools.
  2. Capture sample data.

Checkpoint: Raw dataset stored.

Phase 2: Analysis & Reporting (4-5 days)

Goals: Normalize and score findings.

Tasks:

  1. Build analyzer.
  2. Generate report.

Checkpoint: Deterministic report generated.

Phase 3: Validation (2-3 days)

Goals: Validate rules and handle edge cases.

Tasks:

  1. Add failure tests.
  2. Document runbook.

Checkpoint: Failure cases documented.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Report format JSON, CSV JSON Structured and diffable
Scoring Simple, Weighted Weighted Prioritize high risk findings

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit Tests Parser logic Sample data parsing
Integration Tests End-to-end run Generate report
Edge Case Tests Missing permissions Error path

6.2 Critical Test Cases

  1. Report generated with deterministic ordering.
  2. Exit code indicates failure on invalid output path.
  3. At least one high-risk finding is flagged in test data.

6.3 Test Data

Provide a small fixture file with one known suspicious artifact.

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
Noisy results Too many alerts Add normalization and thresholds
Missing permissions Script fails Detect and warn early

7.2 Debugging Strategies

  • Log raw inputs before normalization.
  • Add verbose mode to show rule evaluation.

7.3 Performance Traps

Scanning large datasets without filtering can be slow; restrict scope to critical paths.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add a Markdown summary report.

8.2 Intermediate Extensions

  • Add a JSON schema validator for output.

8.3 Advanced Extensions

  • Integrate with a SIEM or ticketing system.

9. Real-World Connections

9.1 Industry Applications

  • Security operations audits and detection validation.
  • osquery - endpoint inventory

9.3 Interview Relevance

  • Discussing detection workflows and auditability.

10. Resources

10.1 Essential Reading

  • Practical Malware Analysis - rootkit detection chapters

10.2 Video Resources

  • Conference talks on rootkit detection

10.3 Tools & Documentation

  • OS-native logging and audit tools

11. Self-Assessment Checklist

11.1 Understanding

  • I can describe the trust boundary for this task.

11.2 Implementation

  • Report generation is deterministic.

11.3 Growth

  • I can explain how to operationalize this check.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Report created and contains at least one finding.

Full Completion:

  • Findings are categorized with remediation guidance.

Excellence (Going Above & Beyond):

  • Integrated into a broader toolkit or pipeline.