PrimSynth: An Agentic Framework for Autonomous Linux Kernel Exploit Synthesis

PrimSynth is a novel multi-agent, closed-loop framework designed to automate the synthesis of complex exploit chains for Linux kernel vulnerabilities. By bridging the conceptual gap between high-level exploitation objectives and low-level technical primitives, the system treats exploitation as a formal synthesis problem rather than simple pattern matching. It utilizes a multi-agent architecture to discover, validate, and upgrade exploit primitives within a rebootable sandbox environment. Evaluating across 16 real-world CVEs, the framework demonstrated a 100% primitive extraction accuracy and a 61.3% Strategy Synthesis Rate (SSR) in fully autonomous scenarios. This represents a significant shift from executing existing Proof-of-Concepts (PoCs) to the autonomous generation of new exploitation code for unpatched or unknown vulnerabilities.

• Research Overview: Bridging the Exploit Conceptual Gap

  • Traditional kernel exploitation requires manual translation of high-level goals, such as privilege escalation, into granular technical operations.
  • PrimSynth addresses this bottleneck by treating exploitation as a formal synthesis problem rather than a search for known patterns.
  • The framework shifts the paradigm from "exploit execution" (running existing code) to "exploit synthesis" (generating novel code).

• Methodology: Multi-Agent Synthesis and Validation

  • Formalized Primitive Taxonomy: Classifies exploit primitives into six distinct categories based on their logical capability and validated effect.
  • Vulnerability-Directed Execution Engine: Employs targeted execution to extract and identify specific primitives within a live kernel.
  • Rebootable Validation Environment: Uses a specialized, safe sandbox to iteratively test primitive validity without compromising host system stability.
  • Extended Exploit Strategy Representation: Couples primitive upgrading strategies with formal code synthesis rules, including temporal sequencing and environment prerequisites.

• Key Findings: Benchmarking Autonomous Synthesis

  • Primitive Extraction Accuracy: Achieved a perfect 100% match rate across 16 real-world Linux kernel CVEs spanning five vulnerability types.
  • Guided Strategy Synthesis (SSR): Reached an 82.4% success rate when the framework was provided with existing public PoC code for guidance.
  • Autonomous Strategy Synthesis (SSR): Demonstrated a 61.3% success rate when operating entirely without external primitive hypotheses or PoC guidance.

• Industry Implications: Defensive Challenges in the AI Era

  • Weaponization Speed: The ability to synthesize exploits autonomously significantly accelerates the window between vulnerability discovery and weaponization.
  • Dual-Use Risk: The framework provides a blueprint for highly capable, automated offensive AI capable of targeting unpatched zero-day vulnerabilities.
  • Evolving Defense Requirements: Highlights the critical need for advanced kernel-level telemetry and automated detection of sophisticated, multi-stage memory corruption chains.

• Conclusion

  • PrimSynth represents a fundamental shift toward autonomous, agentic offensive security operations in the kernel space.
  • The framework's ability to operate effectively without prior PoCs poses a significant long-term challenge to traditional patch-based defense models.