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Quick Definition (30–60 words)

Syscall filtering controls which kernel system calls a process can invoke. Analogy: a customs checkpoint that only allows specific items into a country. Formal: a kernel-enforced policy that permits, denies, or conditions execution of syscall interfaces to limit attack surface and enforce least privilege.

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What is syscall filtering?

Syscall filtering is the practice of restricting or monitoring which kernel-level system calls an application or container can make. It is not a substitute for proper application design or user-space security, but a hardening layer that reduces capability exposure and attack surface.

Key properties and constraints:

• Kernel-enforced at runtime.
• Can be whitelist-first (deny by default) or blacklist-first.
• May include argument inspection or only syscall numbers.
• Adds overhead and complexity; policies need maintenance.
• Behavior varies across kernels, distributions, and sandboxing frameworks.

Where it fits in modern cloud/SRE workflows:

• Security hardening for cloud workloads and containers.
• Defense-in-depth paired with least-privilege IAM, network policies, and image scanning.
• Used in production to reduce blast radius of zero-days and misconfigurations.
• Integrated into CI/CD pipelines and automated policy management.

Text-only diagram description readers can visualize:

• Process tree with each process boxed; arrows from processes to kernel boundary filtered by a policy gate. Policy sources: agent, init, container runtime. Events logged to observability pipeline. Alerts trigger runbooks.

syscall filtering in one sentence

A kernel-level policy that enforces which system calls a process may execute to reduce attack surface and control runtime behavior.

syscall filtering vs related terms (TABLE REQUIRED)

ID | Term | How it differs from syscall filtering | Common confusion T1 | Seccomp | Narrower runtime sandboxing mechanism | Confused as generic syscall filtering T2 | AppArmor | File and capability focused, not syscall-only | People assume it covers all syscalls T3 | SELinux | Mandatory access control, policy-driven but broader | Thought to filter syscalls directly T4 | ptrace | Tracing API, can intercept syscalls but not policy-first | Mistaken for a lightweight filter T5 | eBPF | Can inspect and filter but is programmable and broader | Assumed identical to seccomp T6 | Kernel module | Can implement filtering but is custom and privileged | Seen as simpler than userspace filters T7 | LSM | Hook-based security modules, not purely syscall lists | Assumed to be same as syscall whitelist

Row Details (only if any cell says “See details below”)

• None

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Why does syscall filtering matter?

Business impact:

• Reduces risk of breaches by limiting actions exploited by malware.
• Lowers potential regulatory and reputational damage by reducing successful attacks.
• Can reduce incident response cost and downtime, protecting revenue and trust.

Engineering impact:

• Fewer runtime exploits lead to lower incident frequency.
• Improves mean time to recovery by narrowing root-cause possibilities.
• Requires engineers to document and maintain syscall requirements, which can improve design understanding.

SRE framing:

• SLIs: successful requests without policy denials.
• SLOs: target minimal syscall-denial-induced errors.
• Error budget: account for fallout from strict policies during rollouts.
• Toil: initial policy creation is toil; automation reduces recurring work.
• On-call: need clear runbooks for syscall-denial incidents.

What breaks in production (realistic examples):

1) Image migration: New runtime uses different libc leading to blocked syscalls and application crashes. 2) Upgrade rollback: Kernel change exposes new syscall numbers causing denials. 3) Third-party library update: Adds JIT or tracing that requires execve or ptrace. 4) Batch job change: Uses perf or futex with arguments that filtered policy blocks. 5) High-performance tuning: Attempts to use io_uring but policy lacks allowances.

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Where is syscall filtering used? (TABLE REQUIRED)

ID | Layer/Area | How syscall filtering appears | Typical telemetry | Common tools L1 | Edge — reverse proxies | Sandboxed proxy processes with syscall limits | Policy denial logs, request errors | seccomp, eBPF L2 | Network — sidecars | Sidecars limited to minimal syscalls | Denial events, latency spikes | container runtime hooks L3 | Service — microservices | Per-service runtime policies | App errors, crash loops | seccomp profiles L4 | App — binary workloads | Hardened binaries using filters | Audit logs, core dumps | libseccomp L5 | Data — DB processes | Limited admin syscalls for DB engines | Transaction errors, OOM | chroot plus seccomp L6 | IaaS | VM agents with syscall policies | Host audit logs, agent health | kernel modules, eBPF L7 | PaaS | Platform enforces profiles per app | Deployment failures, SLO alerts | platform-managed seccomp L8 | SaaS | Multi-tenant sandboxes | Tenant isolation logs | custom sandboxing L9 | Kubernetes | Pod-level seccomp profiles | Pod events, K8s audit | PodSecurity, CRI runtimes L10 | Serverless | Function runtime minimal syscalls | Invocation errors, cold starts | managed runtime policies L11 | CI/CD | Build/test sandboxing | Build failures, test flakiness | containers, runners L12 | Observability | Agents limited by filter | Missing telemetry, agent restarts | eBPF, restricted agents

Row Details (only if needed)

• None

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When should you use syscall filtering?

When necessary:

• Exposed to untrusted code or multi-tenant environments.
• Regulatory or risk assessment requires strict runtime controls.
• Running high-value assets where exploit risk is high.

When optional:

• Internal services with trusted teams and short lifespan.
• Dev environments where iteration speed matters more than hardening.

When NOT to use / overuse:

• During large-scale development without automation for policy management.
• For applications that require broad syscall sets (e.g., low-level runtimes) unless redesigned.

Decision checklist:

• If handling untrusted input AND multi-tenancy -> enforce strict filters.
• If performance-sensitive AND stable internal code -> consider lighter filters.
• If frequent library updates -> automate policy generation and testing.

Maturity ladder:

• Beginner: Use platform-provided default profiles and monitor denials.
• Intermediate: Generate per-service profiles via CI, gate policy changes.
• Advanced: Automated policy synthesis, continuous validation, argument-level filtering, and rollback-safe canaries.

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How does syscall filtering work?

Step-by-step overview:

1) Policy creation: define allowed syscalls and conditions. 2) Policy distribution: embed in container image or load via runtime. 3) Kernel enforcement: kernel checks each syscall against policy. 4) Action: allow, deny, log, or kill process based on rule. 5) Telemetry: audit logs and metrics shipped to observability. 6) Response: alerts trigger runbooks and potential automated rollback.

Components and workflow:

• Policy authoring tools (manual or generator).
• Runtime loader (container runtime or OS service).
• Kernel mechanism (seccomp or eBPF program).
• Logging/telemetry agent.
• CI/CD gate and testing harness.

Data flow and lifecycle:

• Author -> CI -> Image -> Runtime -> Kernel enforcement -> Logs -> Observability -> Alerts -> Runbook action.

Edge cases and failure modes:

• Kernel-version mismatches leading to different syscall numbers or behavior.
• Legitimate but rare syscalls blocked causing intermittent failures.
• Side effects like signal handling differences when killed.

Typical architecture patterns for syscall filtering

1) Default deny profile at platform level: good for multi-tenant PaaS. 2) Per-image generated profile via CI: balance safety and specificity. 3) Runtime-loaded adaptive policy: use telemetry to expand policy during canary. 4) eBPF-based conditional filtering: for complex argument checks and dynamic analysis. 5) Dual-mode: deny-with-logging in production first, then enforce after validation.

Failure modes & mitigation (TABLE REQUIRED)

ID | Failure mode | Symptom | Likely cause | Mitigation | Observability signal F1 | Application crash | Process exits unexpectedly | Missing syscall allowance | RL rollback, add rule | Crash count spike F2 | Silent failure | Requests fail without logs | Denied syscall not logged | Enable audit logging | Request error rate rise F3 | Performance regression | Latency increases | Heavy filter overhead or traps | Optimize policy, use eBPF | Latency spike F4 | Policy drift | New releases break | Profile outdated | Automated policy regen | Deployment failure rate F5 | Kernel incompat | Denials on upgrade | Syscall ABI changes | Test upgrades in staging | Post-upgrade denials F6 | Telemetry loss | No denial events | Agent limited by filter | Allow observability syscalls | Missing metrics F7 | Overly permissive | Exploit path remains | Broad allow rules | Harden policy | Security audit warning

Row Details (only if needed)

• None

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Key Concepts, Keywords & Terminology for syscall filtering

Glossary (40+ terms). Each item: Term — definition — why it matters — common pitfall

1. Seccomp — Linux sandboxing mechanism restricting syscalls — Common kernel facility for filters — Mistaken as full security
2. libseccomp — User library to build seccomp policies — Simplifies policy construction — Using defaults without understanding
3. eBPF — In-kernel programmable bytecode for tracing and filtering — Enables complex logic and performance — Complexity and safety concerns
4. syscall — Kernel-facing function like open, read, execve — Fundamental surface to control — Confusing with library calls
5. whitelist — Allow-only policy approach — Lowest privilege model — May block unexpected valid behavior
6. blacklist — Deny-only approach — Easier short-term — Misses unknown threats
7. argument filtering — Conditions on syscall arguments — Granular control for safety — Increased complexity
8. syscall number — Numeric identifier for a syscall — Used by kernel policies — Varies across architectures
9. ABI — Application Binary Interface — Ensures syscall semantics — Kernel/architecture mismatch issues
10. runtime profile — Policy applied at process start — Operational unit of filtering — Outdated profiles cause failures
11. container runtime — Software starting containers (CRI) — Injection point for policies — Varied support across runtimes
12. PodSecurity — Kubernetes admission that can enforce seccomp — Platform-level policy — Misconfigured defaults cause downtime
13. CRI — Container Runtime Interface — How K8s interacts with runtimes — Different runtimes have different capabilities
14. syscall trap — Kernel action when syscall blocked — Can be errno or kill — Hard to diagnose without logs
15. audit log — Kernel or agent log of denials — Primary telemetry source — High volume can be noisy
16. denial mode — Operation mode when syscalls are blocked — Decide between errno or kill — Wrong mode causes crashes
17. errno — Error code returned when syscall denied — Application must handle it — Unexpected errno can break logic
18. kill mode — Kernel sends SIGKILL on violation — Immediate hard failure — Harder to recover from
19. profile generation — Creating policy automatically from traces — Speeds adoption — Needs good test coverage
20. policy drift — Policies that lag code changes — Causes production failures — Requires CI checks
21. canary rollout — Gradual deployment technique — Limits blast radius — Requires monitoring for denials
22. auditd — Daemon collecting kernel audit events — Useful for long-term analysis — High cardinality issues
23. observability agent — Agent shipping logs/metrics — Must be allowed by policy — Agent failures blind teams
24. CI gating — Tests that validate profiles before merge — Prevents regressions — Hard to implement for complex apps
25. syscall enumeration — Listing required syscalls for an app — Baseline for profiles — Incomplete lists break apps
26. fuzzing — Testing with random inputs to find unexpected syscalls — Improves policy coverage — Resource intensive
27. kernel hook — LSM or module entrypoints — Alternative enforcement points — Risky if custom modules used
28. performance overhead — Extra processing for syscall checks — Affects latency-sensitive apps — Use efficient mechanisms
29. tracing — Capturing syscall usage — Needed for profile generation — Generates sensitive data
30. sandbox — Runtime environment with limited capabilities — System-level isolation — Overly strict sandboxes break developers
31. privilege escalation — Gaining higher rights via syscalls — Filter mitigates many vectors — Not a panacea
32. capability — POSIX capability like CAP_NET_RAW — Related to syscalls but different model — Confused with syscall filters
33. seccomp profile schema — JSON representation of rules — Portable across systems — Schema mismatch across versions
34. architecture-specific — x86_64 vs arm64 differences — Policies must account for this — Cross-arch issues
35. signal handling — How processes receive signals when blocked — Important for graceful fallback — Neglected in policies
36. logging level — Verbosity of denial reporting — Controls noise — Too low hides issues
37. forensic evidence — Logs used in postmortems — Critical for incident analysis — Missing logs hamper RCA
38. automated remediation — Scripts to adjust policies based on patterns — Reduces toil — Risky without guardrails
39. runtime hooks — Callbacks in runtime during start — Place to inject profiles — Varied runtime support
40. syscall interception — Catching syscalls for policy or tracing — Basis for filters — Performance and compatibility tradeoffs
41. compatibility matrix — Supported features across kernels and runtimes — Needed for planning — Often out of date
42. operator pattern — Kubernetes operator managing profiles — Automates lifecycle — Needs RBAC controls
43. multiline profile — Profiles with multiple conditions — Enables fine-grained policy — Complex to test
44. denylist evolution — Lifecycle of blocked items — Governance needed — Uncontrolled expansion breaks apps

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How to Measure syscall filtering (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID | Metric/SLI | What it tells you | How to measure | Starting target | Gotchas M1 | Denial rate | Fraction of calls denied | Deny events / total syscalls | 0.01% for mature | High volume noisy M2 | Denial-related errors | User errors due to denial | App errors with denial correlation | <0.1% of errors | Hard to correlate M3 | Deny per deployment | Regressions caused by new deploy | Denials in 30m after deploy | 0 per deploy | Canary needed M4 | Agent telemetry loss | Observability agent denials | Missing metrics count | 0 incidents/mo | Agents often filtered M5 | Time to recover | MTTR for denial-induced incidents | Time from alert to resolution | <30m for critical | Runbook readiness critical M6 | Policy drift window | Age between code change and policy update | Median days | <7 days | Requires automation M7 | Latency delta | Overhead introduced by filtering | P90 latency with vs without policy | <5% delta | Load variation affects results M8 | Canary pass rate | Success of canary with policy | Requests pass canary / total | 99% pass | Small samples noisy M9 | False positive rate | Legitimate calls blocked | Postmortem classification | <0.1% | Requires tagging M10 | Coverage | Fraction of observed syscalls included | syscalls allowed / observed | 95% during profiling | Dynamic behavior increases

Row Details (only if needed)

• None

Best tools to measure syscall filtering

Tool — Prometheus

• What it measures for syscall filtering: Denial counters, latency, agent health.
• Best-fit environment: Kubernetes and containerized platforms.
• Setup outline:
• Export denial counters from agent.
• Scrape metrics via service discovery.
• Configure recording rules for rates.
• Strengths:
• Powerful query language.
• Works with alerting.
• Limitations:
• Not for high-cardinality event logs.
• Requires instrumentation.

Tool — OpenTelemetry

• What it measures for syscall filtering: Traces linking denials to requests.
• Best-fit environment: Distributed services with tracing.
• Setup outline:
• Instrument apps to emit trace spans on failure.
• Correlate denial events with traces.
• Collect metrics from spans.
• Strengths:
• Full request context.
• Vendor-neutral.
• Limitations:
• Additional overhead.
• Requires tracing adoption.

Tool — Fluentd/Fluent Bit

• What it measures for syscall filtering: Collects kernel audit logs and denial events.
• Best-fit environment: Log-heavy systems.
• Setup outline:
• Configure source for audit logs.
• Parse and route denial events.
• Forward to central store.
• Strengths:
• Lightweight collector.
• Flexible routing.
• Limitations:
• Parsing complexity.
• Backpressure handling.

Tool — eBPF tooling (bcc, bpftrace)

• What it measures for syscall filtering: Live syscall traces and argument inspection.
• Best-fit environment: Linux hosts with kernel support.
• Setup outline:
• Run probes for syscall entry/exit.
• Aggregate patterns into metrics.
• Use in staging for policy generation.
• Strengths:
• High-fidelity tracing.
• Low overhead if tuned.
• Limitations:
• Kernel version dependencies.
• Learning curve.

Tool — CI policy validators

• What it measures for syscall filtering: Validates profiles against test suites.
• Best-fit environment: CI/CD pipelines.
• Setup outline:
• Run test matrix with profile enforcement.
• Fail pipeline on denials.
• Generate report artifacts.
• Strengths:
• Prevents regressions.
• Enforces gating.
• Limitations:
• Requires comprehensive tests.
• Potential flakiness.

Recommended dashboards & alerts for syscall filtering

Executive dashboard:

• Panels: Denial rate trend, incidents caused by denials, MTTR, policy drift window.
• Why: High-level health and business risk indicators.

On-call dashboard:

• Panels: Current denial rate, recent denial events stream, canary status, impacted services list.
• Why: Rapid diagnosis and prioritization.

Debug dashboard:

• Panels: Per-pod denial counts, syscall type breakdown, correlated traces, recent policy versions.
• Why: Root-cause analysis and remediation.

Alerting guidance:

• Page vs ticket: Page for service-impacting denials and high denial-related error rates. Ticket for policy drift and low-severity increases.
• Burn-rate guidance: If denial-induced error budget burn exceeds 20% of remaining budget in an hour, page.
• Noise reduction tactics: Aggregate denials per deployment, rate-limit similar events, dedupe by cluster and pod, use suppression windows during known rollouts.

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Implementation Guide (Step-by-step)

1) Prerequisites: – Inventory of target workloads. – Kernel and runtime compatibility matrix. – Observability pipeline and logging. – CI integration points.

2) Instrumentation plan: – Add syscall tracing in staging. – Collect baseline events for representative workloads. – Tag traces with deployment metadata.

3) Data collection: – Use eBPF or strace-like tooling to record syscalls. – Store samples in accessible format. – Retain context for correlation with requests.

4) SLO design: – Define SLOs for denial impact and MTTR. – Set enforcement gates in CI for new policies.

5) Dashboards: – Create executive, on-call, and debug dashboards. – Add per-environment filters.

6) Alerts & routing: – Set alert thresholds and routing rules. – Implement suppression for controlled rollouts.

7) Runbooks & automation: – Create runbooks for denial incidents. – Automate rollback of enforcement mode if blast radius exceeds threshold.

8) Validation (load/chaos/game days): – Run load tests with policies applied. – Perform chaos tests: disable allowance, force denials. – Conduct game days to exercise runbooks.

9) Continuous improvement: – Periodically regenerate profiles from production telemetry. – Review postmortems and update policies.

Pre-production checklist:

• Policy validated against integration tests.
• Observability confirms no agent denial.
• Canary environment with canary profile.

Production readiness checklist:

• Automated rollback in place.
• Runbooks tested and on-call trained.
• SLOs set and dashboards live.

Incident checklist specific to syscall filtering:

• Identify affected service and deployment.
• Check denial logs and correlate traces.
• Switch enforcement to logging-only if available.
• Rollback recent deployment if needed.
• Update policy and redeploy once validated.

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Use Cases of syscall filtering

1) Multi-tenant PaaS isolation – Context: Many tenants run arbitrary apps. – Problem: Tenant escape and privilege escalation risk. – Why filtering helps: Limits kernel interfaces to reduce attack surface. – What to measure: Denial rates per tenant, isolation incidents. – Typical tools: seccomp, container runtime profiles.

2) Hardened edge proxies – Context: Exposed reverse proxy handling untrusted traffic. – Problem: Exploits targeting proxy runtime. – Why filtering helps: Blocks uncommon syscalls used in RCE chains. – What to measure: Denials during anomalous traffic spikes. – Typical tools: seccomp, eBPF.

3) CI runner sandboxing – Context: Shared build machines executing code. – Problem: Malicious build scripts attempting host access. – Why filtering helps: Prevents dangerous syscalls like mount or ptrace. – What to measure: Build denials, host intrusion attempts. – Typical tools: containers, seccomp.

4) Observability agent protection – Context: Agents with wide access are attractive targets. – Problem: Compromise leads to broad data exfiltration. – Why filtering helps: Limit agent to required syscalls only. – What to measure: Agent telemetry loss, denied attempts. – Typical tools: libseccomp, policy testing.

5) Database hardening – Context: Database servers storing critical data. – Problem: Kernel exploits can lead to data theft. – Why filtering helps: Blocks admin-level syscalls for runtime. – What to measure: Denials during maintenance windows. – Typical tools: seccomp, capability reduction.

6) Serverless function sandboxing – Context: High churn functions running user-provided code. – Problem: Functions trying to access host resources. – Why filtering helps: Minimal syscall surface reduces escape risk. – What to measure: Invocation errors, cold-start latency. – Typical tools: managed platform policies, eBPF.

7) Legacy app containment – Context: Old apps with unknown behavior. – Problem: Unexpected syscalls used by legacy code. – Why filtering helps: Enforce whitelists and observe deviations. – What to measure: Frequency of legacy-only syscalls. – Typical tools: strace to generate profiles, then seccomp.

8) High-performance runtime protection – Context: Services using io_uring or special syscalls. – Problem: Need granular control without harming performance. – Why filtering helps: Allow only needed fast-path syscalls with eBPF. – What to measure: Latency delta, denial counts. – Typical tools: eBPF, kernel tuning.

9) Compliance-driven environments – Context: Regulated systems requiring runtime controls. – Problem: Certifications may require runtime protections. – Why filtering helps: Provides measurable controls. – What to measure: Audit logs retention and policy versions. – Typical tools: auditd, seccomp.

10) Rapid incident containment – Context: Live exploit detected in one service. – Problem: Stop lateral movement quickly. – Why filtering helps: Apply restrictive profile to affected service to block common exploit syscalls. – What to measure: Reduction in suspicious syscalls, containment time. – Typical tools: runtime hooks, orchestration automation.

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Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes pod hardening with seccomp

Context: Microservice in Kubernetes handling external traffic.
Goal: Prevent runtime escapes while minimizing disruptions.
Why syscall filtering matters here: Containers can be exploited to access the host; per-pod filtering reduces risk.
Architecture / workflow: Generate profile in CI from integration tests; attach seccomp profile to Pod spec via annotation or PodSecurity. Telemetry from kubelet and audit logs into observability.
Step-by-step implementation:

1) Trace syscalls in staging under load. 2) Generate minimal profile. 3) Run canary pod with logging-only enforcement. 4) Monitor denials and adjust. 5) Gradually promote to enforce mode for production. What to measure: Denial counts per pod, request error rate, MTTR.
Tools to use and why: libseccomp for profile format, Kubernetes PodSecurity, eBPF for profiling.
Common pitfalls: Using overly strict profile when profiling lacked full workload behavior.
Validation: Canary results showing zero user-visible errors and low denial tail.
Outcome: Reduced attack surface with monitored rollout and ability to rollback.

Scenario #2 — Serverless function runtime protection

Context: Managed FaaS platform executing user functions.
Goal: Ensure functions cannot access host resources or escape.
Why syscall filtering matters here: High churn and untrusted code raise risk.
Architecture / workflow: Platform enforces runtime policy at function sandbox creation. Denials routed to logging backplane.
Step-by-step implementation:

1) Define minimal syscall baseline for functions. 2) Test with a representative function suite. 3) Enforce policy at platform layer. 4) Monitor invocation errors and cold-start latency. What to measure: Invocation error rate, cold-start delta, denial events.
Tools to use and why: Built-in managed policies, eBPF for conditional checks.
Common pitfalls: Blocking syscalls needed by legitimate language runtimes.
Validation: Controlled release across tenants with telemetry showing negligible impact.
Outcome: Stronger isolation with maintainable operational overhead.

Scenario #3 — Incident response and postmortem

Context: Zero-day exploit triggered remote code execution on a service.
Goal: Contain exploit and prevent lateral movement.
Why syscall filtering matters here: Can stop exploit chains by denying critical syscalls.
Architecture / workflow: Orchestration applies emergency restrictive profile to affected service; observability shows reduction in malicious syscalls.
Step-by-step implementation:

1) Detect anomaly via IDS. 2) Apply emergency profile in high-availability mode. 3) Trace for residual activity and collect forensic logs. 4) Patch and harden images. 5) Update CI to include hardened profile. What to measure: Time to containment, number of processes killed, denied exploit syscalls.
Tools to use and why: Runtime hooks, automation, logging pipeline.
Common pitfalls: Emergency profile causes collateral outages if too strict.
Validation: Postmortem shows containment within target MTTR.
Outcome: Faster containment and improved policies preventing recurrence.

Scenario #4 — Cost vs performance trade-off when using eBPF

Context: High-throughput service considering eBPF for argument-level filtering.
Goal: Achieve fine-grained filtering with minimal latency impact.
Why syscall filtering matters here: Need to block specific exploit patterns without degrading throughput.
Architecture / workflow: Deploy eBPF probes in canary nodes to test overhead then scale.
Step-by-step implementation:

1) Profile baseline performance. 2) Implement eBPF program in staging. 3) Run load tests and measure P95/P99. 4) Optimize eBPF maps and aggregation. 5) Gradually roll out to production. What to measure: Latency delta, CPU overhead, denial accuracy.
Tools to use and why: bpftrace, production telemetry.
Common pitfalls: Kernel or distro incompatibilities causing rollbacks.
Validation: Load test shows <5% P95 delta.
Outcome: Fine-grained protection with acceptable performance trade-off.

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Common Mistakes, Anti-patterns, and Troubleshooting

List of mistakes with Symptom -> Root cause -> Fix

1) Symptom: Immediate crashes after deployment -> Root cause: Enforcement kill mode on unexpected syscall -> Fix: Switch to errno or logging mode and iterate. 2) Symptom: Missing telemetry after policy applied -> Root cause: Observability agent syscalls blocked -> Fix: Allow agent syscalls or run agent outside restricted sandbox. 3) Symptom: High denial noise -> Root cause: Profile generated on incomplete traces -> Fix: Retrace with broader workload and longer windows. 4) Symptom: Slow rollouts -> Root cause: Manual policy edits per service -> Fix: Automate profile generation and CI gating. 5) Symptom: App errors only in production -> Root cause: Environment-specific syscalls (e.g., cloud-specific) -> Fix: Include staging with production-like environment. 6) Symptom: Performance regression -> Root cause: Inefficient eBPF or trap-heavy policies -> Fix: Optimize policy paths and prefer in-kernel checks. 7) Symptom: Cross-architecture failures -> Root cause: Syscall number differences on arm64 -> Fix: Build arch-aware policies. 8) Symptom: False positives in denial mapping -> Root cause: Denial logs not correlated with request context -> Fix: Enrich logs with request IDs. 9) Symptom: Unsuccessful CI gating -> Root cause: Flaky tests triggering false denials -> Fix: Stabilize test suite and use retries sparingly. 10) Symptom: Policy explosion -> Root cause: Ad-hoc allow additions without review -> Fix: Governance and periodic pruning. 11) Symptom: Long RCA cycles -> Root cause: No forensic logs retained -> Fix: Retain and index denial logs for postmortem. 12) Symptom: Developer friction -> Root cause: Lack of self-service policy preview -> Fix: Provide tooling and fast feedback loops. 13) Symptom: Agent OOMs when parsing logs -> Root cause: High volume audit logs -> Fix: Sampling, aggregation, or rate-limiting. 14) Symptom: Denials only under load -> Root cause: Race conditions requiring syscalls at high concurrency -> Fix: Load-test during profiling. 15) Symptom: Denials after kernel upgrade -> Root cause: ABI or behavior change -> Fix: Test kernel upgrades in staging. 16) Symptom: Too permissive policies -> Root cause: Overbroad wildcards in profiles -> Fix: Narrow rules and use argument checks. 17) Symptom: Non-deterministic failures -> Root cause: Timing dependencies on specific syscalls -> Fix: Capture longer traces and debug. 18) Symptom: Lack of ownership -> Root cause: No team assigned to policies -> Fix: Assign accountability and on-call rotation. 19) Symptom: Ineffective canaries -> Root cause: Canary workload not representative -> Fix: Use production-like canaries. 20) Symptom: Misrouted alerts -> Root cause: Alert rules not scoped by service -> Fix: Add labels and proper routing. 21) Symptom: Security audit failures -> Root cause: Audit logs incomplete or missing -> Fix: Ensure retention and completeness. 22) Symptom: Policy conflict with LSMs -> Root cause: Conflicting security modules like AppArmor -> Fix: Evaluate combined policy interactions. 23) Symptom: High cardinality metrics -> Root cause: Per-process telemetry without aggregation -> Fix: Aggregate by service or rule. 24) Symptom: Overreliance on automation leading to mistakes -> Root cause: No human review on significant rule changes -> Fix: Approve critical policy updates. 25) Symptom: Observability blind spots -> Root cause: Logs filtered before export -> Fix: Allow essential telemetry syscalls and paths.

Observability pitfalls (at least 5 included above):

• Missing telemetry due to agent filtering.
• High cardinality from unaggregated denial events.
• Poor correlation between denials and request traces.
• Excessive audit log volume overwhelming pipelines.
• Insufficient retention hampering postmortems.

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Best Practices & Operating Model

Ownership and on-call:

• Policy owners per service or team; security owns platform defaults.
• On-call engineers trained on syscall denial runbooks.
• Escalation path between SRE/security and developers.

Runbooks vs playbooks:

• Runbooks: step-by-step for operational incidents (switch to logging-only, rollback).
• Playbooks: strategic escalation and remediation across teams.

Safe deployments (canary/rollback):

• Start in logging-only mode for canaries.
• Use small percentage traffic canaries and monitor denial impact.
• Automate rollback thresholds based on SLOs.

Toil reduction and automation:

• Automate profile generation, CI validation, and policy promotion.
• Periodic audits to prune unused allowances.
• Provide developer tooling to preview profile changes.

Security basics:

• Always pair syscall filtering with least privilege, capabilities reduction, and network segmentation.
• Keep audit logs immutable and centrally stored for forensics.
• Regularly test kernel and runtime upgrades in staging.

Weekly/monthly routines:

• Weekly: Review denial spikes and failed deployments.
• Monthly: Policy pruning and coverage reports.
• Quarterly: Run game days and maturity reviews.

What to review in postmortems related to syscall filtering:

• Time-to-detection and containment based on denial logs.
• Policy version history and why a change occurred.
• Test coverage that would have caught the issue.
• Automation gaps and what to add to CI.

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Tooling & Integration Map for syscall filtering (TABLE REQUIRED)

ID | Category | What it does | Key integrations | Notes I1 | seccomp | Kernel-level syscall filtering | container runtimes, libseccomp | Common default I2 | libseccomp | Builds seccomp profiles | CI, build pipelines | Used to compile JSON I3 | eBPF | Dynamic tracing and filtering | Observability, security agents | Powerful and flexible I4 | PodSecurity | K8s admission for policies | Kubernetes API | Platform-level enforcement I5 | Container runtime | Loads profiles into containers | CRI, kubelet | Varies by runtime I6 | Auditd | Collects kernel audit events | SIEM, logging backends | High volume tool I7 | CI validators | Tests profiles during CI | GitOps, pipelines | Prevents regressions I8 | Tracing (OTel) | Correlates denials to requests | APM, observability | Context-rich I9 | Log collectors | Forward denial logs | Elasticsearch, object stores | Central store requirement I10 | Policy managers | Lifecycle management of profiles | Git, operator patterns | Governance needed I11 | IDS/IPS | Detects exploit patterns | SIEM, alerting | Complements filtering I12 | Runtime operator | Automates profile rollout | Kubernetes operator | Easier scale

Row Details (only if needed)

• None

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Frequently Asked Questions (FAQs)

H3: What is the difference between seccomp and eBPF?

Seccomp is a kernel feature for basic syscall filtering; eBPF is a programmable in-kernel runtime that can trace and enforce complex logic.

H3: Will syscall filtering prevent all exploits?

No. It reduces attack surface but does not eliminate vulnerabilities in user-space or other kernel subsystems.

H3: Does syscall filtering affect performance?

It can; naive implementations may add latency. Use efficient mechanisms and test under realistic load.

H3: How do I start with syscall filtering for my service?

Start with tracing in staging, generate a whitelist profile, run canaries in logging-only mode, then enforce gradually.

H3: Can syscall filters be updated at runtime?

Depends on mechanism and runtime. Some runtimes support loading profiles on start or via admin APIs.

H3: How do I debug a denial in production?

Correlate denial logs with request IDs and traces, check recent deployments, and switch enforcement to logging-only if needed.

H3: Are syscall numbers the same across architectures?

No. They can differ by architecture; generate arch-specific profiles.

H3: Should I block syscalls entirely or use argument checks?

Prefer allow lists; argument checks are powerful but add complexity and maintenance cost.

H3: How do I prevent observability blind spots?

Ensure observability agents are allowed necessary syscalls and test telemetry during policy validation.

H3: Can I automate policy generation?

Yes, using traces and eBPF, but require representative workloads and CI validation.

H3: What logging is essential for postmortems?

Timestamps, process IDs, pod/deployment labels, syscall name and args, and policy version.

H3: How to handle third-party libraries requiring special syscalls?

Isolate them, add targeted allowances, or run them in separate privileged runtime as last resort.

H3: Is syscall filtering compatible with serverless platforms?

Yes; many managed platforms enforce similar restrictions. Verify platform capabilities.

H3: How often should profiles be reviewed?

At least monthly or after significant code changes.

H3: What should be in the runbook for a syscall denial incident?

Immediate rollback steps, logs to inspect, how to switch to logging-only, and escalation contacts.

H3: Will syscall filtering interfere with debuggers?

Yes; ptrace and other debugging syscalls are often blocked, hindering live debugging.

H3: How to test policies in CI?

Run integration tests with policies applied in ephemeral environments and fail builds on denials.

H3: Can syscall filtering be applied to VMs?

Yes; through kernel modules or agent-based mechanisms, but support varies.

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Conclusion

Syscall filtering is a practical and powerful layer in modern defense-in-depth strategies. It reduces attack surface, assists incident containment, and provides measurable controls when combined with good observability and automation. Adopt a staged approach: trace, generate, canary, and enforce, with CI validation and runbooks to manage risk.

Next 7 days plan:

• Day 1: Inventory workloads and kernel/runtime compatibility.
• Day 2: Enable syscall tracing in staging for key services.
• Day 3: Generate initial seccomp profiles and run in logging-only mode.
• Day 4: Build CI checks to validate profiles against integration tests.
• Day 5: Create dashboards and alerts for denials and policy drift.
• Day 6: Execute a canary rollout for one non-critical service.
• Day 7: Run a short game day exercising denial incident runbooks.

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Appendix — syscall filtering Keyword Cluster (SEO)

• Primary keywords
• syscall filtering
• syscall sandboxing
• seccomp profile
• seccomp filtering
• kernel syscall filter
• syscall whitelist
• syscall denylist
• runtime syscall policy
• syscall enforcement

• syscall security

• Secondary keywords

• libseccomp usage
• eBPF syscall filtering
• container syscall hardening
• Kubernetes seccomp
• PodSecurity seccomp
• syscall telemetry
• audit syscall denials
• tracing syscalls
• argument-based filtering

• syscall policy automation

• Long-tail questions

• how to create a seccomp profile for a container
• best practices for syscall filtering in kubernetes
• how does syscall filtering affect performance
• how to debug seccomp denial in production
• can eBPF replace seccomp for syscall filtering
• how to automate seccomp profile generation
• what syscalls are needed by a java runtime
• how to correlate syscall denials with traces
• what is the difference between seccomp and eBPF
• how to handle syscall changes after kernel upgrade
• how to run canary rollouts for syscall enforcement
• how to prevent observability blind spots with seccomp
• how to test syscall filters in CI pipelines
• how to allow observability agents in restricted sandboxes
• how to log syscall denials for postmortems
• how to implement syscall filtering in serverless
• how to measure syscall filtering impact on latency
• how to apply syscall filtering to legacy applications
• how to manage policy drift for syscall profiles

• how to reduce toil from maintaining syscall policies

• Related terminology

• system call
• syscall number
• ABI differences
• kernel audit
• runtime profile
• policy generation
• enforcement mode
• logging-only mode
• deny-to-errno
• deny-to-kill
• capability reduction
• LSM (Linux Security Modules)
• AppArmor
• SELinux
• container runtime interface
• pod annotation
• observability agent
• CI gating
• canary deployment
• MTTR
• SLI
• SLO
• auditd
• bpftrace
• eBPF map
• syscall tracer
• runtime hook
• operator pattern
• policy manager