Architectural Invariant Design: eBPF BPF-LSM Inode Digest Provenance Enforcement for Container Executables
eBPF BPF-LSM Inode Digest Provenance Enforcement for Container Executables defines an industrial-grade behavioral standard for security engineering. Enforces kernel-level execution authorization via the BPF-LSM bprm_check_security hook by verifying immutable cryptographic inode digests against container cgroup manifests. Blocks unauthenticated binary execution, runtime drift, and fileless memory injection before process address spaces are allocated, eliminating the race conditions and user-space overhead of ptrace or auditd. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.
Hazard Analysis: What Breaks Without This Rule
The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:
| Failure Mode | AI Symptom (The Anti-Pattern) | Severity | Production Impact & Remedy |
|---|---|---|---|
| AI Hallucination & Deprecation Drift | Path-Based String Filtering and Asynchronous Audit Tracepoints for Container Execution Auditing | Critical | Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production. Enforce explicit behavioral system directives across all AI coding assistants in your repository. |
| Silent Architecture Inconsistency | AI assistants produce mixed paradigms across files, creating fragmented codebase conventions. | Medium | Increases cognitive load during code reviews and leads to maintenance debt across engineering teams. Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails. |
Step-by-Step Production Directives
Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:
Engineers MUST ALWAYS implement execution blocking via LSM hooks (specifically 'lsm/bprm_check_security') rather than non-blocking tracepoints or kprobes.
Engineers MUST ALWAYS implement execution blocking via LSM hooks (specifically 'lsm/bprm_check_security') rather than non-blocking tracepoints or kprobes.
Engineers MUST NOT rely on binary file paths or string matching (such as bprm->filename or dentry names) for provenance validation, as paths are mutable and vulnerable to symlink pivots and TOCTOU races.
Engineers MUST NOT rely on binary file paths or string matching (such as bprm->filename or dentry names) for provenance validation, as paths are mutable and vulnerable to symlink pivots and TOCTOU races.
Security policies MUST evaluate execution permissions synchronously by looking up the cryptographic digest (IMA measurement or fs-verity root) in a pinned BPF hash-of-maps keyed by container cgroup ID.
Security policies MUST evaluate execution permissions synchronously by looking up the cryptographic digest (IMA measurement or fs-verity root) in a pinned BPF hash-of-maps keyed by container cgroup ID.
Programs MUST fail-closed
Programs MUST fail-closed: return -EPERM or -EACCES immediately if the target binary's digest is absent, unverified, or mismatched against the container's cryptographically signed manifest.
Memory allocations and map operations within the BPF fast-path MUST NOT perform unbounded loops or invoke non-reentrant BPF helpers that compromise real-time microservice throughput.
Memory allocations and map operations within the BPF fast-path MUST NOT perform unbounded loops or invoke non-reentrant BPF helpers that compromise real-time microservice throughput.
Frequently Asked Technical Questions
Common questions developers face when configuring AI coding assistants for this stack:
Q:Why is eBPF BPF-LSM Inode Digest Provenance Enforcement for Container Executables critical for AI coding assistants?
Enforces kernel-level execution authorization via the BPF-LSM bprm_check_security hook by verifying immutable cryptographic inode digests against container cgroup manifests. Blocks unauthenticated binary execution, runtime drift, and fileless memory injection before process address spaces are allocated, eliminating the race conditions and user-space overhead of ptrace or auditd. Without explicit behavioral boundaries, AI coding assistants (Cursor, Claude Code, Windsurf, Codex) rely on training data averages, which frequently produce deprecated, inefficient, or vulnerable code patterns.
Q:How does adopting this rule prevent AI hallucinations?
This rule explicitly declares what the AI must avoid (Path-Based String Filtering and Asynchronous Audit Tracepoints for Container Execution Auditing) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.
Q:Which AI coding environments are supported?
This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).
Q:Can I combine this rule with other technology stacks?
Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.