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Quick Definition (30–60 words)

A 0-day is a previously unknown vulnerability or exploit that has no available vendor patch or public mitigation at discovery time; think of it as an unlocked door you didn’t know existed. Formally: a vulnerability with zero days of public disclosure or vendor remediation time.

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What is 0-day?

A 0-day refers to a software or hardware vulnerability that is unknown to the vendor or defender at the time it is discovered by an attacker or researcher, and for which no official patch or mitigation is available. It is not a finished exploit campaign by default; it becomes dangerous when weaponized or integrated into attack chains.

What it is NOT:

• Not every newly-discovered bug is a 0-day; only those unknown to the vendor or without a patch qualify.
• Not synonymous with “zero trust” or “zero configuration”; different domains.

Key properties and constraints:

• Unpatched: No vendor-provided fix exists.
• Unknown to vendor or defenders: Disclosure hasn’t triggered a vendor response.
• High secrecy value: Attackers try to keep it private to maximize impact.
• Time-limited: Once disclosed or patched, it ceases to be a 0-day.
• Validation complexity: Determining exploitability and scope takes time.

Where it fits in modern cloud/SRE workflows:

• Security teams integrate 0-day threat intelligence into incident response and patching policies.
• SREs evaluate blast radius, rollback strategies, and mitigation automation.
• Cloud architects plan network segmentation, runtime defenses, and layered mitigations to reduce 0-day impact.
• CI/CD and infra-as-code pipelines include security gates and automated mitigations where feasible.

Text-only diagram description:

• “User traffic flows to edge layer, then to load balancer, then microservices. 0-day exists in library used by service A. Exploit triggers code path, attacker gains access to service A, then lateral movement to service B via shared credentials, then data exfiltration to external endpoint.” Visualize arrows: Edge -> LB -> Service A (vulnerable) -> Service B -> Data exfiltration.

0-day in one sentence

A 0-day is a vulnerability unknown to the vendor with no available patch, creating an immediate window of exploitable risk.

0-day vs related terms (TABLE REQUIRED)

ID	Term	How it differs from 0-day	Common confusion
T1	Vulnerability	A broader category; 0-day is a subset that is unpatched	Confused as any bug being a 0-day
T2	Exploit	Exploit is the code that uses a 0-day	People use exploit and 0-day interchangeably
T3	Zero-click	A type of exploit that needs no user action; can be 0-day	Not all zero-click issues are 0-days
T4	Patch	Patch is remediation; 0-day lacks one	Some say patched 0-day which is contradictory
T5	Disclosure	The act of informing vendor or public; 0-day exists before public disclosure	Confused with responsible disclosure timelines
T6	Vulnerability Window	Time between discovery and patch; 0-day is start of window	People conflate entire window with 0-day
T7	CVE	Identifier assigned on disclosure; 0-day often has none yet	People expect a CVE for every 0-day immediately
T8	RCE	Remote code execution is a class of exploit; may be 0-day	Not every RCE is a 0-day

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

• None

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Why does 0-day matter?

Business impact:

• Revenue risk: Data breaches or service interruptions can directly reduce revenue and incur fines.
• Trust erosion: Customers lose confidence after breaches; rebuilding trust is costly.
• Regulatory exposure: Unpatched compromises can trigger compliance violations.

Engineering impact:

• Incident churn: 0-day incidents create high-severity pages, increased toil, and context switching.
• Velocity slowdown: Patch-and-harden cycles reduce feature delivery velocity.
• Technical debt surfacing: Old libraries and shared components become high-risk.

SRE framing:

• SLIs/SLOs: 0-day can spike error rates, increase latency, and cause availability SLO violations.
• Error budgets: Rapid burn of error budget can force rollbacks or feature freezes.
• Toil & on-call: Handling 0-day increases toil with triage, mitigation, and coordination tasks.

What breaks in production — realistic examples:

1. Container escape via outdated runtime library leads to host compromise and lateral movement.
2. Image processing library vulnerability allows RCE in a public upload endpoint causing data exfiltration.
3. Privilege escalation in IAM token service allows attackers to mint long-lived credentials.
4. Serverless cold-start vulnerability used to run arbitrary code at scale, causing billing spikes.
5. Database engine craft payload exposes customer PII from multi-tenant service.

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Where is 0-day used? (TABLE REQUIRED)

This table maps where 0-day issues typically appear across architecture, cloud, and ops layers.

ID	Layer/Area	How 0-day appears	Typical telemetry	Common tools
L1	Edge / CDN	Vulnerable parsing or cache poisoning exploits	Error spikes and unusual cache misses	WAF, CDN logs
L2	Network / Load balancer	Protocol handling flaws or buffer overflows	Connection resets and anomalous packets	IDS, packet capture
L3	Service / App	Library vuln or logic bug leading to RCE	High error rates and suspicious execs	APM, service logs
L4	Container runtime	Escape via runtime bug	Host alerts and unexpected containers	Container runtime logs, host telemetry
L5	Orchestration layer	Kubernetes CVE in Kubelet/apiserver	Pod restarts and permission spikes	K8s audit logs, control plane metrics
L6	Serverless / FaaS	Function isolation bug	Invocation anomalies and billing spikes	Cloud function metrics, traces
L7	Data layer	DB engine exploit or SQL injection variant	Slow queries and anomalous exports	DB logs, query audit
L8	CI/CD pipeline	Pipeline agent compromise or artifact poisoning	Build failures and unexpected artifacts	CI logs, artifact registry
L9	IAM / Tokens	Token signing or issuance flaw	Unauthorized token usage	Auth logs, token issuance logs
L10	SaaS dependent services	Third-party app vuln affecting tenants	Multi-tenant error patterns	SaaS provider logs and telemetry

Row Details (only if needed)

• None

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When should you use 0-day?

Note: “Use 0-day” here means handling, prioritizing, or building defenses specific to 0-day risk.

When it’s necessary:

• Active exploit observed in the wild against assets you own.
• Indicators of compromise tie to unknown vulnerabilities in critical infrastructure.
• Threat intelligence flags targeted 0-day campaigns against your sector.

When it’s optional:

• Harden non-critical systems where resource constraints exist.
• Red-team exercises simulating plausible 0-day scenarios.
• Early upgrades where vendor patching is risky and mitigations suffice temporarily.

When NOT to use / overuse it:

• Avoid over-prioritizing unverified 0-day leads that distract from clear operational risks.
• Don’t replace standard patching and hygiene with chasing unconfirmed 0-day threats.

Decision checklist:

• If exploit observed and asset critical -> Immediate containment and emergency response.
• If exploit unverified but TTPs match high risk -> Increase monitoring and apply mitigations.
• If vendor patch available -> Apply patch using staged rollout and canary.
• If unknown impact and non-critical -> Treat as vulnerability management item and schedule patching.

Maturity ladder:

• Beginner: Focus on patch management, asset inventory, and basic network segmentation.
• Intermediate: Add runtime detection, automated mitigations, and threat intel integration.
• Advanced: Full automation for containment, provenance tracing, adaptive defenses, and offensive testing for 0-day resilience.

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How does 0-day work?

Step-by-step explanation of a typical 0-day lifecycle in an attack context:

1. Discovery: Researcher or attacker finds an exploitable flaw in software or hardware.
2. Weaponization: Attacker develops an exploit or exploit chain against the flaw.
3. Targeting: Attacker identifies targets where the vulnerable software exists.
4. Delivery: The exploit is delivered (network, file upload, malicious link, supply chain).
5. Exploitation: Vulnerability is triggered to achieve code execution or elevation.
6. Post-exploit actions: Persistence, credential theft, lateral movement, data exfiltration.
7. Detection/Disclosure: Defender or third party discovers the event; public disclosure may occur.
8. Patch: Vendor releases patch; defenders must validate and deploy.
9. Remediation & lessons: SREs and security teams update processes to reduce future risk.

Components and workflow:

• Vulnerable component: Binary, library, firmware, or configuration.
• Attacker tooling: Exploit code and delivery mechanisms.
• Entry vectors: Network ports, user uploads, CI artifacts, third-party integrations.
• Telemetry sources: Logs, traces, metrics, IDS/EDR.
• Response actions: Isolate, patch, revocation, rotate credentials.

Data flow and lifecycle:

• Input: Vulnerability details and telemetry signals.
• Processing: Detection rules and enrichment with threat intel.
• Decision: Contain, mitigate, patch, or monitor.
• Output: Remediation actions, alerts, and postmortem artifacts.

Edge cases and failure modes:

• False positives on exploit detection leading to unnecessary outages.
• Partial mitigations that degrade functionality but fail to stop exploit.
• Supply-chain 0-day where patching requires many vendors to act.

Typical architecture patterns for 0-day

1. Defense-in-depth microservices: Multi-layered controls at edge, API gateway, service mesh, and runtime for reduction of blast radius. Use when high multi-tenant risk exists.
2. Network segmentation and zero trust: Strict per-service auth and network policies to prevent lateral movement. Use when regulatory or sensitive data is present.
3. Immutable infrastructure with fast rollback: Replace compromised instances via immutable pipelines rather than patch in place. Use when automation and CI/CD maturity is high.
4. Runtime detection and response (EDR/RASP): Monitor for exploitation behaviors at runtime and block suspicious syscall patterns. Use when rapid detection is priority.
5. Canary and phased patching: Deploy fixes to small subsets first to detect regressions while protecting majority. Use in environments where downtime risk is high.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	False positive block	Service outage after mitigation	Overzealous rule or bad fingerprint	Rollback rule and refine with test cases	Error rate spike on rollout
F2	Incomplete patch deployment	Some nodes remain exploitable	Staggered rollout failure	Force redeploy or quarantine hosts	Mixed versions in inventory
F3	Lateral movement	New hosts compromised after initial breach	Flat network or shared creds	Segment network and rotate creds	Unusual auth events
F4	Credential theft	Long-lived tokens used from new IPs	Poor token rotation policies	Revoke and reissue tokens	Token issuance anomalies
F5	Supply-chain persistence	Reintroduced vuln via CI artifact	Compromised artifact registry	Rebuild artifacts and harden pipeline	New artifact signatures
F6	Alert fatigue	Important alerts missed	Too many noisy alerts	Tune thresholds and dedupe alerts	Alert counts and MTTR rise
F7	Patch regression	New bug after patch	Patch not tested on canary	Rollback and extended testing	Error increase post-patch

Row Details (only if needed)

• None

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Key Concepts, Keywords & Terminology for 0-day

(Note: Each entry is “Term — Definition — Why it matters — Common pitfall”. Kept concise; 40+ terms.)

1. 0-day — Vulnerability unknown to vendor with no patch — Critical immediate risk — Confused with any new bug
2. Exploit — Code or method to abuse vuln — Converts vulnerability to attack — Assuming exploit exists for every vuln
3. Vulnerability — Weakness in software or design — Basis for exploits — Overlooking configuration issues
4. CVE — Identifier for disclosed vuln — Helps tracking — Not every 0-day has one yet
5. Disclosure — Public or private reveal of vuln — Triggers patch life cycle — Premature disclosure can harm defenders
6. Responsible disclosure — Coordinated vendor notification — Balances info flow — Delays can prolong risk
7. Zero-click — Exploit requiring no user action — High severity — Assuming all attacks need user interaction
8. RCE — Remote code execution — Full system compromise risk — Not every RCE is exploitable in context
9. Privilege escalation — Gain higher privileges — Amplifies impact — Ignoring least privilege
10. Lateral movement — Moving between systems post-compromise — Broadens blast radius — Flat networks enable it
11. Supply chain attack — Compromise via dependencies or build pipeline — Hard to detect — Neglecting artifact provenance
12. Patch — Vendor fix — Ends 0-day state — Patch regressions risk availability
13. Hotfix — Emergency patch — Rapid mitigation — Can bypass tests
14. Mitigation — Non-patch control to reduce risk — Buys time — May impair functionality
15. WAF — Web application firewall — Edge mitigation — Rules may be bypassed
16. IDS/IPS — Detection/prevention for network threats — Useful signal — Encrypted traffic limits visibility
17. EDR — Endpoint detection and response — Runtime visibility — Coverage gaps on ephemeral workloads
18. RASP — Runtime application self-protection — In-app mitigation — Performance impact
19. SIEM — Log aggregation and correlation — Centralized detection — Alert overload risk
20. Threat intelligence — Context about active threats — Prioritizes response — Feeds can be noisy
21. Indicators of Compromise — Observable artifacts of attack — Used for containment — IOC mismatch causes misses
22. Bug bounty — Program to find vulns — Incentivizes disclosure — May miss targeted 0-days
23. Responsible disclosure window — Time negotiated for patching — Affects when 0-day becomes public — Varies widely
24. Canary — Small-scale deployment for testing — Reduces regression risk — Too small can miss scenarios
25. Immutable infrastructure — Replace rather than patch in place — Easier rollback — Requires automation discipline
26. Chaos testing — Simulating failures including security incidents — Improves resilience — Not a replacement for security testing
27. Forensic image — Snapshot for investigation — Preserves evidence — Delays remediation if overused
28. Runtime attestations — Proof of integrity for running code — Reduces risk of tampering — Attestation ecosystems vary
29. Artifact signing — Ensures integrity of builds — Prevents artifact substitution — Key management is critical
30. Least privilege — Minimize permissions — Limits exploit impact — Requires granular IAM
31. Multi-tenancy isolation — Separating customer workloads — Reduces blast radius — Misconfiguration undermines it
32. SI/SDLC gating — Security gates in CI/CD — Prevents vulnerable code deployment — Can slow pipeline
33. Hotpatching — Patch without restart — Faster mitigation — Complexity and risk of instability
34. Air gap — Isolated network for critical systems — Limits exposure — Operationally heavy
35. Threat hunting — Proactive search for adversary activity — Finds stealthy activity — Resource intensive
36. Incident response playbook — Predefined steps for breaches — Speeds response — Must be updated for new threats
37. TTPs — Tactics techniques procedures of attackers — Useful for detection rules — Changing attacker behavior reduces value
38. Code signing — Ensures binary provenance — Defends supply chain — Keys must be protected
39. Memory corruption — Common root cause class for 0-days — Leads to RCE — C/C++ codebases more exposed
40. Logic flaw — Design-level weakness — Often high impact — Hard to discover with automated tools
41. Obfuscation — Hiding malicious code — Makes detection harder — Generates false negatives
42. Sandbox breakout — Escaping restricted execution environment — Compromises isolation — Critical for cloud workloads
43. EOL software — End of life components — No patches available — High long-term risk
44. Patch backlog — Unapplied patches across estate — Increases exposure — Resource and compatibility constraints
45. Hotlist / allowlist — Known good indicators — Helps block unknowns — Maintenance burden

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How to Measure 0-day (Metrics, SLIs, SLOs) (TABLE REQUIRED)

This section focuses on practical metrics for tracking 0-day exposure, detection, and response.

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Mean time to detect	Speed of detection	Time from exploit start to detection	< 1 hour for critical systems	Detection gaps skew this metric
M2	Mean time to contain	Time to stop spread	Time from detection to containment action	< 2 hours for critical	Depends on automation maturity
M3	Patch deployment rate	Percent patched within timeframe	Patched hosts divided by total	95% within 7 days	Patching order matters
M4	Vulnerable asset count	Number of assets with known unpatched vulns	Scanning inventory vs vulnerability DB	Decrease trend week over week	False negatives in scanners
M5	Exploit success rate	Percent of attempts that succeeded	Simulated exploit attempts results	As close to 0% as feasible	Ethical limits on testing
M6	Alert-to-incident ratio	Noise level of alerts	Alerts leading to incidents / total alerts	Lower is better but context-specific	Overly strict tuning hides signals
M7	Error budget burn rate	SLO impact during incident	Rate of SLO exhaustion	Maintain buffer for emergencies	Correlate with incident severity
M8	Time to rollback	Time to rollback affected service	Time from decision to restore previous release	< 15 minutes for canary systems	Requires tested rollback paths
M9	Forensic readiness score	Preparedness to analyze an incident	Checklist-based scoring	80%+ readiness	Organizational variability
M10	Threat intel enrichment rate	Contributory intel to detections	Percent of alerts with TI context	Improve monthly	TI quality varies

Row Details (only if needed)

• None

Best tools to measure 0-day

Pick 5–10 tools. For each tool use exact structure.

Tool — SIEM (Security Information and Event Management)

• What it measures for 0-day: Correlation of logs to detect anomalous patterns.
• Best-fit environment: Large organizations with diverse telemetry.
• Setup outline:
• Ingest logs from hosts, containers, K8s control plane, WAF.
• Define correlation rules for known exploit behaviors.
• Integrate threat intelligence feeds.
• Configure alerting and automated playbook triggers.
• Regularly tune for noise reduction.
• Strengths:
• Centralized correlation and long-term retention.
• Good for cross-system detection.
• Limitations:
• High maintenance and can be noisy.
• May miss novel exploit behaviors without good rules.

Tool — EDR (Endpoint Detection and Response)

• What it measures for 0-day: Runtime process behaviors, suspicious telemetry on hosts.
• Best-fit environment: Server fleets and developer workstations.
• Setup outline:
• Deploy agents across hosts and container hosts.
• Configure policies for suspicious syscall patterns.
• Integrate with SOAR for automated containment.
• Strengths:
• Deep runtime visibility.
• Fast containment actions.
• Limitations:
• Coverage gaps on ephemeral containers unless specialized.
• Resource and privacy considerations.

Tool — RASP (Runtime Application Self-Protection)

• What it measures for 0-day: Application-level exploitation attempts and anomalies.
• Best-fit environment: Web and API services.
• Setup outline:
• Instrument app binaries or frameworks.
• Define attack rules and runtime checks.
• Test in staging before enabling blocking in production.
• Strengths:
• In-app context for precise blocking.
• Minimal network dependency.
• Limitations:
• Performance overhead.
• Integration complexity across languages.

Tool — K8s Audit + Policy Engine

• What it measures for 0-day: Control-plane and API misuse indicative of exploitation.
• Best-fit environment: Kubernetes clusters.
• Setup outline:
• Enable audit logs and ship to central store.
• Apply admission controllers for deny-lists.
• Monitor anomalous API patterns.
• Strengths:
• High fidelity for K8s-specific attacks.
• Enforceable policies pre-deployment.
• Limitations:
• Large volume of audit logs.
• May be bypassed if attacker controls control plane.

Tool — Artifact Signing & Registry Scanning

• What it measures for 0-day: Tampered artifacts or vulnerable dependencies.
• Best-fit environment: CI/CD pipelines and container registries.
• Setup outline:
• Enforce signed artifacts for deployment.
• Run dependency scanners on build.
• Block deploys with high-risk findings.
• Strengths:
• Protects supply chain.
• Prevents reintroduction of compromised binaries.
• Limitations:
• False positives on transitive dependencies.
• Requires developer buy-in.

Recommended dashboards & alerts for 0-day

Executive dashboard:

• Panels:
• Overall vulnerable asset count trend: shows exposure over time.
• Active incidents and severity: current 0-day impact summary.
• SLO health summary: aggregate availability and latency impact.
• Patch deployment progress: percent of affected systems patched.
• Business-critical system status: uptime for key services.
• Why: Provides leadership with concise risk posture and remediation progress.

On-call dashboard:

• Panels:
• Real-time alerts tied to containment actions.
• Detection timeline for the active incident.
• Service health and latency/error panels for impacted services.
• Recent deploys and rollback controls.
• Runbook quick links and escalation contacts.
• Why: Gives responders the context to act fast.

Debug dashboard:

• Panels:
• Per-instance logs and traces for the affected service.
• Process and syscall anomalies.
• Network connections and outbound endpoints.
• Authentication and token issuance events.
• Canary test results and rollback status.
• Why: Deep visibility for engineers triaging root cause.

Alerting guidance:

• Page vs ticket:
• Page: Active exploitation observed or SLO-critical service degradation.
• Ticket: Suspicious but unverified anomalies or low-impact vulnerabilities.
• Burn-rate guidance:
• If error budget burn rate exceeds 500% over a rolling 1-hour window, escalate to page.
• Noise reduction tactics:
• Dedupe alerts across sources.
• Group related alerts by incident ID.
• Suppress low-priority alerts during active incident handling to reduce cognitive load.

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Implementation Guide (Step-by-step)

1) Prerequisites: – Asset inventory and dependency map. – Centralized logging and tracing. – CI/CD pipelines with immutable artifacts. – Incident response playbooks and communication channels.

2) Instrumentation plan: – Identify high-risk components and add enhanced telemetry. – Instrument syscall traces, runtime metrics, and binary integrity checks. – Ensure K8s audit logs and control plane metrics are collected.

3) Data collection: – Consolidate logs into SIEM or observability backend. – Capture network flows and process-level metrics. – Store immutable forensic snapshots on suspected hosts.

4) SLO design: – Define availability and integrity SLOs for critical services. – Allocate error budgets specifically for security incidents. – Create guardrails that trigger emergency response when burned quickly.

5) Dashboards: – Build executive, on-call, and debug dashboards described earlier. – Include drill-down links from executive to on-call to debug.

6) Alerts & routing: – Map alert severities to on-call rotation and escalation. – Automate initial containment actions where safe. – Integrate with ticketing and communication platforms.

7) Runbooks & automation: – Create playbooks for detection, containment, patching, and communication. – Automate routine steps like token rotation, canary redeploy, and quarantine.

8) Validation (load/chaos/game days): – Run tabletop exercises for 0-day scenarios. – Execute chaos tests to validate containment and rollback. – Perform game days that simulate supply-chain and runtime exploitation.

9) Continuous improvement: – Post-incident retrospectives to refine tooling and playbooks. – Update SLIs and SLOs from lessons learned. – Rotate detection rules and test against new threat intel.

Checklists:

Pre-production checklist:

• Asset inventory verified.
• Dependency scanning enabled in CI.
• Canary deployment path tested.
• RASP or runtime probes integrated into staging.
• Alert routing tested to on-call.

Production readiness checklist:

• Central logging and auditing enabled.
• Incident playbooks present and accessible.
• Backups and recoveries tested.
• Patch rollback plan validated.
• MFA and credential rotation policies applied.

Incident checklist specific to 0-day:

• Triage and confirm exploit evidence.
• Quarantine affected hosts or services.
• Rotate credentials tied to affected components.
• Capture forensic data and preserve chain of custody.
• Communicate to stakeholders and follow disclosure policy.

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Use Cases of 0-day

Provide 8–12 use cases, each concise.

1. Public-facing image service – Context: Service accepts user uploads and processes images. – Problem: Image library 0-day leads to RCE. – Why 0-day helps: Understanding risk prioritizes containment and patch cycles. – What to measure: RCE indicators, error rates, file processing anomalies. – Typical tools: WAF, EDR, RASP.

2. Multi-tenant database cluster – Context: Shared DB for many customers. – Problem: Engine 0-day allows cross-tenant data access. – Why 0-day helps: Forces urgent isolation and migration strategy. – What to measure: Query patterns, data export volumes, auth logs. – Typical tools: DB auditing, SIEM.

3. Kubernetes control plane exploit – Context: K8s apiserver vulnerability discovered. – Problem: Cluster takeover possible. – Why 0-day helps: Prioritize control plane hardening and network policies. – What to measure: K8s audit anomalies, pod creation patterns. – Typical tools: K8s audit, admission controllers.

4. CI/CD compromise – Context: Build agents run untrusted code. – Problem: Artifact poisoning via 0-day in runner. – Why 0-day helps: Triggers artifact signing and rebuilds. – What to measure: Registry changes, build provenance, pipeline logs. – Typical tools: Artifact signing, CI logs.

5. Serverless function isolation bug – Context: Multi-tenant serverless platform. – Problem: Sandbox breakout via 0-day. – Why 0-day helps: Immediate scaling down and migration to isolated accounts. – What to measure: Invocation patterns and cross-function communication. – Typical tools: Cloud function telemetry and runtime guards.

6. Edge device firmware 0-day – Context: Fleet of IoT devices at edge. – Problem: Wormable exploit across devices. – Why 0-day helps: Prioritizes OTA patch plan and network isolates. – What to measure: Telemetry heartbeats and firmware versions. – Typical tools: Device management platform.

7. Token signing service bug – Context: Auth service signing tokens. – Problem: 0-day allows forged tokens. – Why 0-day helps: Force token revocation and rotation. – What to measure: Token issuance and validation failures. – Typical tools: Auth logs and JWT blacklists.

8. Third-party SaaS dependency – Context: Critical SaaS provider has a 0-day. – Problem: Service degradation or data leakage potential. – Why 0-day helps: Trigger contingency plans and data export limits. – What to measure: Integration errors and data transfer rates. – Typical tools: API gateway telemetry and contract testing.

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Scenario Examples (Realistic, End-to-End)

Four scenarios including required types.

Scenario #1 — Kubernetes control plane exploit

Context: A critical internal cluster hosts customer workloads. Goal: Detect and contain control plane exploit rapidly. Why 0-day matters here: Apiserver or kubelet 0-day can permit cluster-wide takeover. Architecture / workflow: K8s control plane with RASP in pods and EDR on nodes; audit logs shipped to SIEM. Step-by-step implementation:

1. Enable audit logs and send to SIEM.
2. Deploy network policies to limit pod-to-pod and pod-to-host access.
3. Configure admission controllers to deny privileged containers.
4. Set automated quarantine for nodes showing anomalous kubelet activity. What to measure: K8s audit anomalies, pod creation spikes, host process anomalies. Tools to use and why: K8s audit, admission controllers, SIEM, EDR. Common pitfalls: Too many audit logs causing missed signals; admission controller misconfig. Validation: Game day simulating apiserver compromise with canary cluster. Outcome: Faster containment, reduced lateral movement, validated rollback.

Scenario #2 — Serverless function sandbox breakout (serverless/managed-PaaS)

Context: Multi-tenant functions in managed cloud provider. Goal: Reduce impact from a potential sandbox breakout 0-day. Why 0-day matters here: Isolation breach can affect other tenants and billing. Architecture / workflow: Functions behind API gateway, per-tenant VPCs, strict IAM roles. Step-by-step implementation:

1. Log full invocation context and outbound network calls.
2. Enforce least privilege IAM for functions.
3. Throttle and set egress deny-by-default with allowlist.
4. Create emergency function rollback and credential rotation automation. What to measure: Outbound connections, invocation anomaly rate, cost spikes. Tools to use and why: Cloud function telemetry, WAF, IAM monitoring. Common pitfalls: Overly restrictive egress blocks legitimate behavior. Validation: Simulated exploit that attempts host access; confirm isolation holds. Outcome: Containment without downtime, automated mitigation in place.

Scenario #3 — Incident-response postmortem using 0-day indicators (incident-response/postmortem)

Context: Production service was breached via unknown exploit. Goal: Determine whether a 0-day was used and patch response improvements. Why 0-day matters here: Identifying a 0-day affects disclosure and patch urgency. Architecture / workflow: Forensic imaging, SIEM correlation, code review of dependencies. Step-by-step implementation:

1. Preserve forensic snapshots of affected hosts.
2. Correlate IOCs with threat intel and known exploits.
3. Reproduce exploit in lab environment safely.
4. Work with vendor responsible disclosure and timeline. What to measure: Time to detection, time to containment, patch time. Tools to use and why: SIEM, forensic tools, isolated testbeds. Common pitfalls: Destroying volatile evidence during containment. Validation: Re-run reproduction post-patch to ensure fix. Outcome: Clear postmortem, vendor patch, improved detection rules.

Scenario #4 — Cost vs performance trade-off during 0-day remediation (cost/performance trade-off scenario)

Context: Hotfix for a vulnerable image processing service increases CPU usage. Goal: Balance security patching with cost and latency SLA. Why 0-day matters here: Rapid patch raises compute costs and may degrade latency. Architecture / workflow: Microservices on autoscaling groups with APM and cost monitoring. Step-by-step implementation:

1. Deploy patch to canary with performance monitoring.
2. Run load tests to measure CPU and latency changes.
3. If regressions severe, apply temporary mitigation (rate-limit) and plan optimized patch.
4. Scale compute for critical windows and optimize later. What to measure: Latency percentiles, CPU utilization, error rates, cost per request. Tools to use and why: APM, cost monitoring, CI load testing. Common pitfalls: Immediate full rollout without canary causing SLA breach. Validation: Canary under production-like load before full rollout. Outcome: Mitigated exploit risk while managing cost and performance impacts.

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Common Mistakes, Anti-patterns, and Troubleshooting

List of mistakes with symptom -> root cause -> fix. Include observability pitfalls.

1. Symptom: Missing inbound exploit activity. Root cause: No telemetry on edge. Fix: Enable WAF and edge logging.
2. Symptom: False positive mitigation causing outage. Root cause: Block rules too broad. Fix: Implement canary rules and staged rollout.
3. Symptom: Slow detection. Root cause: Logs not centralized. Fix: Ship logs to SIEM and set correlation rules.
4. Symptom: Exploit reappears after patch. Root cause: Compromised artifact in CI. Fix: Rebuild and sign artifacts; rotate keys.
5. Symptom: High alert volume. Root cause: Poor rule tuning. Fix: Deduplicate and tune thresholds.
6. Symptom: Can’t prove 0-day usage. Root cause: No forensic snapshots. Fix: Capture memory and disk images early.
7. Symptom: Lateral movement after initial containment. Root cause: Flat network and shared creds. Fix: Enforce segmentation and rotate creds.
8. Symptom: Patch causes regression. Root cause: No canary testing. Fix: Add canary gates and rollback automation.
9. Symptom: Inconsistent vulnerability counts. Root cause: Inaccurate asset inventory. Fix: Reconcile inventory and automate discovery.
10. Symptom: Missed API misuse patterns. Root cause: No API gateway logging. Fix: Enable detailed gateway logs.
11. Symptom: EDR blind spots on containers. Root cause: Ephemeral workloads not instrumented. Fix: Use container-aware EDR or sidecar.
12. Symptom: Overreliance on vendor patch speed. Root cause: No mitigation plan. Fix: Create mitigation runbooks and compensating controls.
13. Symptom: Too many stakeholders in incident response. Root cause: Unclear roles. Fix: Define ownership and RACI for incidents.
14. Symptom: Alerts triggered but no context. Root cause: No trace correlation. Fix: Instrument tracing and link logs to traces.
15. Symptom: Forensics delayed by legal processes. Root cause: No pre-approved legal workflows. Fix: Predefine legal and PR playbooks.
16. Symptom: Ignoring non-code attack vectors. Root cause: Focus only on app code. Fix: Include infra and config in vulnerability scanning.
17. Symptom: Alert suppression hides real attacks. Root cause: Overaggressive suppression windows. Fix: Review suppression policies periodically.
18. Symptom: High mean time to contain. Root cause: Manual containment steps. Fix: Automate quarantine and mitigation.
19. Symptom: Can’t reproduce exploit. Root cause: Environment drift. Fix: Maintain reproducible build and test environments.
20. Symptom: Observability costs balloon. Root cause: Unbounded telemetry. Fix: Implement sampling and retention policies.
21. Symptom: Detection rules age out. Root cause: No scheduled rule review. Fix: Quarterly threat hunting and rule updates.
22. Symptom: Blindness to outbound exfil. Root cause: No egress monitoring. Fix: Monitor outbound flows and DNS anomalies.
23. Symptom: No SLO priority during incidents. Root cause: Missing error budget policy for security. Fix: Define SLO burn policies tied to incidents.
24. Symptom: Relying solely on signatures. Root cause: Signature-based detection only. Fix: Add behavior-based detection.
25. Symptom: Overprivileged CI runners used by attackers. Root cause: Excessive permissions. Fix: Harden runner IAM and use short-lived tokens.

Observability pitfalls included above: missing telemetry, uncentralized logs, trace gaps, unbounded telemetry costs, suppression masking incidents.

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Best Practices & Operating Model

Ownership and on-call:

• Designate security owner and SRE owner per critical service.
• On-call rotations should include security liaison during high-risk periods.
• Create clear escalation paths and RACI for 0-day incidents.

Runbooks vs playbooks:

• Runbooks: Step-by-step operational procedures for containment and rollback.
• Playbooks: High-level frameworks for incident decision making and stakeholder communication.
• Maintain both and keep them versioned and testable.

Safe deployments:

• Canary deployments and automated rollbacks as default.
• Feature flags to disable risky functionality quickly.
• Staged patching based on exposure and criticality.

Toil reduction and automation:

• Automate containment actions like credential rotation and host quarantine.
• Automate artifact rebuilds and signed deployments.
• Use templated runbooks and SOAR playbooks for repetitive tasks.

Security basics:

• Enforce least privilege and MFA.
• Maintain up-to-date dependency scanning.
• Segment networks and services.

Weekly/monthly routines:

• Weekly: Review new high-severity vulnerabilities and patch progress.
• Monthly: Threat hunting focused on novel TTPs and review of detection rules.
• Quarterly: Game days simulating 0-day scenarios and test runbooks.

Postmortem reviews related to 0-day:

• Verify detection timelines and root cause analysis.
• Document mitigations and patch rollout effectiveness.
• Assess communication timelines and stakeholder impact.
• Update SLOs, dashboards, and runbooks with lessons learned.

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Tooling & Integration Map for 0-day (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	SIEM	Correlates logs and alerts	EDR, K8s audit, WAF	Central for cross-system detection
I2	EDR	Runtime host visibility	SIEM, SOAR	Good for containment actions
I3	RASP	In-app protection	APM, CI	Best for web apps and APIs
I4	WAF	Edge request filtering	CDN, SIEM	First line defense for HTTP
I5	K8s audit	Control plane activity logs	SIEM, policy engines	Essential for K8s clusters
I6	Artifact signing	Ensures artifact integrity	CI, registry	Protects supply chain
I7	Dependency scanner	Finds vulnerable libraries	CI, SCA	Catch known vulnerabilities
I8	SOAR	Automates response playbooks	SIEM, ticketing	Reduces toil
I9	Forensics tools	Image and memory capture	EDR, SIEM	Required for investigations
I10	Admission controllers	Enforce policies pre-deploy	K8s, CI	Prevent risky deployments

Row Details (only if needed)

• None

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Frequently Asked Questions (FAQs)

What exactly is a 0-day?

A 0-day is a vulnerability unknown to the vendor with no available patch at discovery time.

Are 0-days common?

Varies / depends on software; some ecosystems have more frequent discoveries than others.

How do attackers find 0-days?

Through research, fuzzing, reverse engineering, or by analyzing complex code paths.

Should I disclose a found 0-day publicly?

Follow responsible disclosure; public disclosure before a patch can increase risk.

Can automation detect 0-day exploits?

Automation can detect patterns and anomalous behaviors but may not catch novel exploits alone.

How long does a 0-day remain dangerous?

Until a patch is applied and broadly deployed or the exploit is otherwise mitigated.

What is the best immediate action when a 0-day is suspected?

Containment: isolate affected systems, rotate creds, and gather forensic evidence.

How do you prioritize patching for 0-days?

Prioritize by asset criticality, exposure, and potential blast radius.

Can canary deployments help with 0-day patches?

Yes, canaries help test patches for regressions before full rollout.

Should SRE teams own 0-day response?

SREs collaborate with security; ownership should be clearly defined per org.

How do you balance security patches with performance impact?

Use canaries, performance tests, and temporary compensating controls while optimizing fixes.

What metrics are most important for 0-day incidents?

Mean time to detect, mean time to contain, patch deployment rate, and vulnerable asset count.

Are bug bounties effective at finding 0-days?

They can help but may not uncover targeted or sophisticated 0-day research.

How do you handle vendor-supplied 0-days in SaaS?

Follow vendor advisories, apply vendor mitigations, and activate contingency plans when needed.

Can serverless platforms be completely safe from 0-days?

No system is completely safe; serverless reduces some attack surface but introduces its own risks.

What role does threat intel play with 0-days?

TI informs detection and prioritization by indicating active campaigns and indicators.

When should an incident be disclosed to customers?

Disclosure timing depends on legal, regulatory, and risk considerations; follow policy.

Are hardware 0-days handled differently than software 0-days?

Yes; hardware often requires firmware patches or device replacement and can be harder to mitigate.

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Conclusion

0-day vulnerabilities represent urgent, time-sensitive risks that require coordinated detection, containment, and remediation across security and SRE teams. The modern cloud-native landscape—Kubernetes, serverless, CI/CD—demands layered defenses, automated mitigations, and practiced incident response to minimize impact. Treat 0-day preparedness as a cross-functional capability: inventory, telemetry, automation, and rehearsed playbooks are your strongest defenses.

Next 7 days plan:

• Day 1: Verify asset inventory and high-risk dependency list.
• Day 2: Ensure centralized logging and K8s audit are enabled and flowing.
• Day 3: Implement canary pipelines and validate rollback automation.
• Day 4: Create or update 0-day runbook and map stakeholders.
• Day 5: Run tabletop exercise simulating a 0-day in a critical service.

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Appendix — 0-day Keyword Cluster (SEO)

Primary keywords

• 0-day vulnerability
• zero-day exploit
• zero day vulnerability
• zero-day patch
• zero day exploit

Secondary keywords

• 0-day detection
• 0-day mitigation
• zero-day response
• zero-day lifecycle
• zero-day SRE
• zero-day cloud security
• zero-day incident response
• zero-day threat intelligence
• 0-day vulnerability management
• 0-day worm

Long-tail questions

• what is a 0-day vulnerability and how is it discovered
• how to detect zero day exploits in production
• best practices for handling 0-day vulnerabilities in Kubernetes
• how to measure response time for zero day incidents
• can canary deployments help mitigate zero day patches
• how to integrate threat intelligence for 0-day detection
• steps to perform postmortem after zero day breach
• how to harden serverless against zero day exploits
• what telemetry is needed to detect 0-day exploits
• how to automate containment for zero day incidents
• how to prioritize patching when multiple zero days are reported
• what is the role of SRE in zero day response
• how to manage vendor-disclosed zero-day vulnerabilities
• how to prepare CI/CD pipelines for zero day supply chain attacks
• how to balance performance and security during zero day remediation

Related terminology

• CVE
• exploit chain
• responsible disclosure
• runtime protection
• EDR
• RASP
• WAF
• SIEM
• SOAR
• K8s audit
• canary deployment
• artifact signing
• dependency scanning
• least privilege
• network segmentation
• immutable infrastructure
• chaos testing
• forensic imaging
• token rotation
• admission controller
• supply chain security
• memory corruption
• logic flaw
• sandbox breakout
• artifact registry
• hotpatching
• patch backlog
• forensic readiness
• threat hunting
• TTPs
• IOC detection
• error budget
• SLO burn rate
• observability strategy
• telemetry retention
• anomaly detection
• nested privilege escalation
• zero-click exploit
• image processing vulnerability
• serverless isolation