Overview attacks on DNS¶
Attack tree (DNS)¶
This attack tree outlines the methodologies for compromising DNS integrity, from classic cache poisoning and sophisticated DDoS amplification to AI-augmented phishing automation and the looming threat of cryptographic harvesting in the post-quantum era.
1. Exploit Protocol Weaknesses [OR]
1.1 Cache Poisoning [AND]
1.1.1 Exploit weak TXID entropy in DoH/DoT/DoQ resolvers [OR]
1.1.1.1 Birthday attack on 16-bit TXID space
1.1.1.2 Timing attacks on resolver response handling
1.1.1.3 Fragment-based poisoning attacks
1.1.2 Side-channel attacks on encrypted DNS [OR]
1.1.2.1 TLS padding oracle attacks on DoT
1.1.2.2 QUIC protocol timing analysis on DoQ
1.1.2.3 HTTP/2 stream correlation attacks on DoH
1.1.3 DNSSEC Exploitation [OR]
1.1.3.1 NSEC/NSEC3 walking for zone enumeration
1.1.3.2 RRSIG timing attacks for key recovery
1.1.3.3 Algorithm downgrade attacks (ECDSA to RSA)
Prerequisite: AND (Attacker can intercept traffic AND resolver lacks full DNSSEC validation)
1.2 DDoS Amplification [OR]
1.2.1 Abuse misconfigured encrypted DNS resolvers [OR]
1.2.1.1 DoQ reflection with large TXT records
1.2.1.2 DoH POST request amplification
1.2.1.3 DoT session resumption attacks
1.2.2 DNSSEC-based amplification [OR]
1.2.2.1 NSEC3 response amplification
1.2.2.2 Large RRSIG reflection attacks
1.2.2.3 DNAME chain exploitation
Prerequisite: AND (Open resolver available AND vulnerable payload size > 1000 bytes)
1.3 Protocol-Specific Vulnerabilities [OR]
1.3.1 QUIC Protocol Exploitation (DoQ) [OR]
1.3.1.1 Connection migration hijacking
1.3.1.2 Stream priority manipulation
1.3.1.3 QUIC spin bit side-channel
1.3.2 HTTP/2 Exploitation (DoH) [OR]
1.3.2.1 HPACK header compression attacks
1.3.2.2 Server push cache poisoning
1.3.2.3 Stream dependency manipulation
1.3.3 TLS Session Attacks (DoT) [OR]
1.3.3.1 Session ticket stealing
1.3.3.2 Pre-shared key exhaustion
1.3.3.3 Certificate transparency log poisoning
2. Attack Encrypted DNS [OR]
2.1 Privacy Leaks [OR]
2.1.1 Metadata Correlation [AND]
2.1.1.1 IP + timestamp correlation across multiple resolvers
2.1.1.2 Query size and timing analysis
2.1.1.3 Server name indication (SNI) monitoring
2.1.2 ML-based fingerprinting [OR]
2.1.2.1 Neural network traffic analysis
2.1.2.2 Query pattern recognition
2.1.2.3 Encrypted traffic classification
2.1.3 Protocol Identification [OR]
2.1.3.1 DoH/DoT/DoQ protocol fingerprinting
2.1.3.2 Application-level protocol detection
2.1.3.3 Middlebox cooperation for traffic analysis
2.2 Downgrade Attacks [AND]
2.2.1 Force fallback to plaintext DNS [OR]
2.2.1.1 TCP RST injection on port 853 (DoT)
2.2.1.2 HTTP/2 GOAWAY frame injection (DoH)
2.2.1.3 QUIC connection close spoofing (DoQ)
2.2.2 Encryption Bypass [OR]
2.2.2.1 Disable ECH (Encrypted Client Hello) in DoH
2.2.2.2 TLS version downgrade attacks
2.2.2.3 QUIC version negotiation manipulation
2.2.3 Middlebox Interference [OR]
2.2.3.1 ISP-level protocol blocking
2.2.3.2 Enterprise firewall policy enforcement
2.2.3.3 Government-mandated protocol filtering
2.3 Certificate Attacks [OR]
2.3.1 CA Compromise [OR]
2.3.1.1 Rogue certificate issuance
2.3.1.2 Intermediate CA exploitation
2.3.1.3 Certificate transparency log poisoning
2.3.2 Client Validation Bypass [OR]
2.3.2.1 Self-signed certificate acceptance
2.3.2.2 Certificate pinning bypass
2.3.2.3 Trust store manipulation
3. Cloud/SaaS Exploits [OR]
3.1 Kubernetes DNS Compromise [AND]
3.1.1 CoreDNS/Etdncache Poisoning [OR]
3.1.1.1 API server compromise
3.1.1.2 ConfigMap manipulation
3.1.1.3 Plugin vulnerability exploitation
3.1.2 NetworkPolicy Bypass [OR]
3.1.2.1 Privileged pod escape
3.1.2.2 Node-level network access
3.1.2.3 Cross-namespace traffic interception
3.1.3 Service Mesh Exploitation [OR]
3.1.3.1 Istio/Linkerd DNS redirection
3.1.3.2 mTLS certificate theft
3.1.3.3 Sidecar proxy manipulation
3.2 Serverless Abuse [OR]
3.2.1 DNS Tunneling [OR]
3.2.1.1 Lambda TXT record exfiltration
3.2.1.2 Cloud Functions DNS over HTTPS
3.2.1.3 Azure Functions private resolver abuse
3.2.2 Resource Exhaustion [OR]
3.2.2.1 DNS query burst attacks
3.2.2.2 Recursion depth exploitation
3.2.2.3 Cache saturation attacks
3.2.3 Cloud Integration Attacks [OR]
3.2.3.1 AWS Route 53 resolver hijacking
3.2.3.2 Google Cloud DNS API abuse
3.2.3.3 Azure Private DNS zone poisoning
3.3 Container Registry Attacks [OR]
3.3.1 Image Pull Manipulation [OR]
3.3.1.1 DNS spoofing for registry redirection
3.3.1.2 MITM attacks on image downloads
3.3.1.3 Cache poisoning for malicious images
3.3.2 Supply Chain Compromise [OR]
3.3.2.1 Malicious library injection via DNS
3.3.2.2 Dependency confusion attacks
3.3.2.3 Package manager DNS hijacking
4. Supply Chain Attacks [OR]
4.1 Registrar Hijacking [AND]
4.1.1 API Key Compromise [OR]
4.1.1.1 Cloudflare token theft
4.1.1.2 AWS Route 53 key leakage
4.1.1.3 Google Domains API abuse
4.1.2 Social Engineering [OR]
4.1.2.1 Registrar support impersonation
4.1.2.2 Post-GDPR WHOIS information gaps
4.1.2.3 Phone number porting attacks
4.1.3 Registry System Attacks [OR]
4.1.3.1 EPP protocol exploitation
4.1.3.2 Registry lock bypass
4.1.3.3 Transfer process manipulation
4.2 Subdomain Takeover [AND]
4.2.1 Dangling Resource Identification [OR]
4.2.1.1 CNAME mapping to unused cloud resources
4.2.1.2 NS record pointing to decommissioned servers
4.2.1.3 MX record targeting disabled services
4.2.2 Malicious Content Deployment [OR]
4.2.2.1 GitHub Pages site cloning
4.2.2.2 S3 bucket takeover
4.2.2.3 Azure Blob Storage hijacking
4.2.3 Persistence Mechanisms [OR]
4.2.3.1 SSL certificate procurement
4.2.3.2 DNS record obfuscation
4.2.3.3 Monitoring evasion techniques
4.3 CDN Compromise [OR]
4.3.1 DNS-Based CDN Manipulation [OR]
4.3.1.1 Edge server cache poisoning
4.3.1.2 Origin DNS spoofing
4.3.1.3 GeoDNS manipulation
4.3.2 Certificate Manipulation [OR]
4.3.2.1 SAN certificate abuse
4.3.2.2 CDN SSL termination bypass
4.3.2.3 Multi-CDN configuration conflicts
5. AI/ML-Augmented Attacks [OR]
5.1 Evasion Techniques [AND]
5.1.1 Reputation System Poisoning [OR]
5.1.1.1 DNS query pattern manipulation
5.1.1.2 Behavioral model contamination
5.1.1.3 Feedback loop exploitation
5.1.2 Query Obfuscation [OR]
5.1.2.1 GAN-generated benign-looking queries
5.1.2.2 CDN traffic mimicry
5.1.2.3 Legitimate domain spoofing
5.1.3 Adaptive Attacks [OR]
5.1.3.1 Reinforcement learning for evasion
5.1.3.2 Genetic algorithm optimisation
5.1.3.3 Transfer learning across networks
5.2 Phishing Automation [AND]
5.2.1 Domain Generation [OR]
5.2.1.1 LLM-generated homograph domains
5.2.1.2 Context-aware typosquatting
5.2.1.3 Cultural adaptation algorithms
5.2.2 Infrastructure Management [OR]
5.2.2.1 Dynamic DNS fast-flux networks
5.2.2.2 Automated certificate procurement
5.2.2.3 Multi-CDN abuse for resilience
5.2.3 Target Identification [OR]
5.2.3.1 NLP-based brand monitoring
5.2.3.2 Social media sentiment analysis
5.2.3.3 Employee behavior prediction
6. Post-Quantum Threats [OR]
6.1 Cryptographic Harvesting [AND]
6.1.1 DNSSEC Record Collection [OR]
6.1.1.1 ECDSA-P256 signature harvesting
6.1.1.2 RSA-2048 key storage
6.1.1.3 NSEC3 chain enumeration
6.1.2 Quantum Decryption Preparation [OR]
6.1.2.1 Long-term encrypted data storage
6.1.2.2 Future decryption capability planning
6.1.2.3 Harvest-then-decrypt campaigns
6.1.3 Transition Period Exploitation [OR]
6.1.3.1 Algorithm confusion attacks
6.1.3.2 Hybrid scheme weaknesses
6.1.3.3 Backward compatibility exploitation
6.2 Quantum Key Distribution Attacks [OR]
6.2.1 QKD Protocol Exploitation [OR]
6.2.1.1 Photon-splitting attacks
6.2.1.2 Fake state attacks
6.2.1.3 Trojans in QKD hardware
6.2.2 Implementation Vulnerabilities [OR]
6.2.2.1 Side-channel attacks on QKD systems
6.2.2.2 Laser intensity manipulation
6.2.2.3 Detector blinding attacks
6.2.3 Integration Attacks [OR]
6.2.3.1 Classical-quantum interface exploitation
6.2.3.2 Key management system compromise
6.2.3.3 Quantum network routing attacks
7. Data Exfiltration Techniques [OR]
7.1 DNS Tunneling [AND]
7.1.1 Protocol Selection [OR]
7.1.1.1 Traditional DNS (TXT, NULL records)
7.1.1.2 DoH/DoT/DoQ encrypted tunneling
7.1.1.3 ICMP-based DNS manipulation
7.1.2 Evasion Methods [OR]
7.1.2.1 Query rate limiting bypass
7.1.2.2 Legitimate traffic blending
7.1.2.3 Multiple resolver rotation
7.1.3 Data Encoding [OR]
7.1.3.1 Base32/64 encoding variations
7.1.3.2 Compression with error correction
7.1.3.3 Fragmentation and reassembly
7.2 Covert Channels [OR]
7.2.1 Timing-Based Exfiltration [OR]
7.2.1.1 Query response timing modulation
7.2.1.2 DNS refresh interval exploitation
7.2.1.3 TTL value manipulation
7.2.2 Storage Channels [OR]
7.2.2.1 DNS cache poisoning with data
7.2.2.2 NSEC3 gap exploitation
7.2.2.3 DNSSEC signature embedding
7.2.3 Behavioral Patterns [OR]
7.2.3.1 Query sequence encoding
7.2.3.2 Resolver selection patterns
7.2.3.3 Domain name generation algorithms
7.3 Exfiltration Infrastructure [OR]
7.3.1 Command and Control [OR]
7.3.1.1 Dynamic domain generation
7.3.1.2 DNS-based payload delivery
7.3.1.3 Dead drop resolvers
7.3.2 Data Processing [OR]
7.3.2.1 Distributed exfiltration aggregation
7.3.2.2 On-the-fly decoding services
7.3.2.3 Cloud function data processing
7.3.3 Persistence Mechanisms [OR]
7.3.3.1 Multiple exfiltration pathways
7.3.3.2 Fallback communication channels
7.3.3.3 Anti-forensic techniques
Nitty gritty risk table¶
Attack Path |
Technical Complexity |
Resources Required |
Risk Level |
Notes |
---|---|---|---|---|
1.1.1.1 Birthday attack on 16-bit TXID space |
Medium |
Low |
Medium |
Feasible with traffic access and many queries; mitigated by DNSSEC and source port randomisation. |
1.1.1.2 Timing attacks on resolver response handling |
High |
Medium |
High |
Requires precise measurements and traffic observation to bias/guess transactions. |
1.1.1.3 Fragment-based poisoning attacks |
High |
Medium |
High |
Exploits IP fragmentation behaviors; success depends on network middleboxes and resolver settings. |
1.1.2.1 TLS padding oracle attacks on DoT |
Very High |
Medium |
High |
Complex cryptographic side-channel; requires tailored interaction with target DoT stack. |
1.1.2.2 QUIC protocol timing analysis on DoQ |
High |
Medium |
High |
Side-channel on QUIC recovery/timing; needs careful lab setup or privileged network vantage. |
1.1.2.3 HTTP/2 stream correlation attacks on DoH |
High |
Medium |
High |
Associates query streams via timing/priority; depends on client/server implementation quirks. |
1.1.3.1 NSEC/NSEC3 walking for zone enumeration |
Medium |
Low |
Medium |
Information disclosure rather than integrity break; rate limits and opt-out reduce impact. |
1.1.3.2 RRSIG timing attacks for key recovery |
Very High |
High |
Medium |
Academic-style side-channel; practical exploitation is difficult on hardened stacks. |
1.1.3.3 Algorithm downgrade attacks (ECDSA to RSA) |
High |
Medium |
High |
Targets mismatched algorithm policies; hinges on fallback/misconfig. |
1.2.1.1 DoQ reflection with large TXT records |
Medium |
Medium |
High |
Leverages path amplification via QUIC; requires open/misconfigured resolvers. |
1.2.1.2 DoH POST request amplification |
Medium |
Medium |
High |
Uses HTTP request semantics for bandwidth multiplication; CDN/proxy behavior matters. |
1.2.1.3 DoT session resumption attacks |
High |
Medium |
Medium |
Abuses TLS resumption tickets to cut cost per query; mitigations include rate and ticket controls. |
1.2.2.1 NSEC3 response amplification |
Medium |
Medium |
High |
DNSSEC negative responses can be large; filtering and minimisation reduce effect. |
1.2.2.2 Large RRSIG reflection attacks |
Medium |
Medium |
High |
Exploits oversised signed responses; best mitigated by response size limits and egress filtering. |
1.2.2.3 DNAME chain exploitation |
Medium |
Medium |
Medium |
Chained indirections inflate responses; effective mainly with weak ACLs. |
1.3.1.1 Connection migration hijacking |
Very High |
High |
High |
Requires QUIC internals knowledge and network control to spoof migration paths. |
1.3.1.2 Stream priority manipulation |
High |
Medium |
Medium |
Exploits scheduler to starve/shape DNS streams; mainly service degradation. |
1.3.1.3 QUIC spin bit side-channel |
Medium |
Low |
Medium |
Traffic analysis vector; limited by optional spin bit and padding. |
1.3.2.1 HPACK header compression attacks |
High |
Medium |
High |
Compression side-channels (e.g., BREACH-style) adapted to DoH; requires precise control/measurement. |
1.3.2.2 Server push cache poisoning |
High |
Medium |
High |
Abuses HTTP/2 push semantics to seed caches with attacker-chosen artifacts. |
1.3.2.3 Stream dependency manipulation |
Medium |
Low |
Medium |
QoS manipulation to infer/perturb query patterns. |
1.3.3.1 Session ticket stealing |
High |
Medium |
High |
Steals TLS/DoT tickets to resume as victim; needs endpoint compromise or MITM on storage. |
1.3.3.2 Pre-shared key exhaustion |
Medium |
Medium |
Medium |
Forces rotation/exhaustion of PSKs; primarily DoS on session setup. |
1.3.3.3 Certificate transparency log poisoning |
Very High |
High |
Medium |
Requires ecosystem-level manipulation; detection and auditing make success difficult. |
2.1.1.1 IP + timestamp correlation across multiple resolvers |
Medium |
Low |
Medium |
Cross-correlation deanonymizes clients using multi-resolver setups. |
2.1.1.2 Query size and timing analysis |
Medium |
Low |
Medium |
Infers domains from packet sizes/timings even when encrypted. |
2.1.1.3 Server name indication (SNI) monitoring |
Low |
Low |
Medium |
Residual metadata (e.g., SNI when ECH disabled) leaks destinations. |
2.1.2.1 Neural network traffic analysis |
High |
Medium |
High |
ML models classify encrypted flows; requires labeled data and training pipeline. |
2.1.2.2 Query pattern recognition |
Medium |
Low |
Medium |
Identifies applications/users by request rhythms across sessions. |
2.1.2.3 Encrypted traffic classification |
High |
Medium |
High |
Generic encrypted flow fingerprinting; scales with telemetry access. |
2.1.3.1 DoH/DoT/DoQ protocol fingerprinting |
Low |
Low |
Medium |
Distinguishes protocols via handshake/behavior traits for policy enforcement or blocking. |
2.1.3.2 Application-level protocol detection |
Medium |
Low |
Medium |
Infers client apps from traffic patterns/URLs used by DoH endpoints. |
2.1.3.3 Middlebox cooperation for traffic analysis |
Medium |
Medium |
High |
Correlated vantage points (ISP/CDN) increase deanonymisation power. |
2.2.1.1 TCP RST injection on port 853 (DoT) |
Medium |
Low |
High |
Active interference to force plaintext fallback; mitigated by hard-fail policies. |
2.2.1.2 HTTP/2 GOAWAY frame injection (DoH) |
High |
Medium |
High |
Requires HTTP/2 manipulation capabilities; targets client fallback logic. |
2.2.1.3 QUIC connection close spoofing (DoQ) |
High |
Medium |
High |
Spoofs transport errors to trigger downgrade; path validation can help. |
2.2.2.1 Disable ECH (Encrypted Client Hello) in DoH |
Medium |
Low |
Medium |
Strips or blocks ECH to expose SNI; depends on network control. |
2.2.2.2 TLS version downgrade attacks |
High |
Medium |
High |
Classical downgrade if endpoints permit weak versions/ciphers. |
2.2.2.3 QUIC version negotiation manipulation |
High |
Medium |
High |
Abuses version negotiation to weaker behavior/performance. |
2.2.3.1 ISP-level protocol blocking |
Low |
Medium |
High |
Coarse-grained interference at scale; policy/legal environment dependent. |
2.2.3.2 Enterprise firewall policy enforcement |
Low |
Low |
Medium |
Localised blocking/inspection; limited to enterprise boundaries. |
2.2.3.3 Government-mandated protocol filtering |
Low |
High |
High |
High-impact nation-scale filtering; requires regulatory authority. |
2.3.1.1 Rogue certificate issuance |
High |
High |
High |
Compromised/abused CA issues certs for DoH/DoT endpoints. |
2.3.1.2 Intermediate CA exploitation |
High |
High |
High |
Targeting subordinate CAs for lateral issuance capability. |
2.3.1.3 Certificate transparency log poisoning |
Very High |
High |
Medium |
Attempts to corrupt ecosystem observability; hard to sustain covertly. |
2.3.2.1 Self-signed certificate acceptance |
Low |
Low |
Medium |
Misconfigured clients accept self-signed/invalid certs. |
2.3.2.2 Certificate pinning bypass |
High |
Medium |
High |
Requires binary patching/hook or proxy control on client. |
2.3.2.3 Trust store manipulation |
Medium |
Medium |
High |
Installs rogue roots/intermediates on endpoints; persistent if unnoticed. |
3.1.1.1 API server compromise |
High |
High |
High |
Kubernetes control-plane breach enables CoreDNS poisoning. |
3.1.1.2 ConfigMap manipulation |
Medium |
Medium |
High |
Alters CoreDNS config/plugins to redirect/poison cluster DNS. |
3.1.1.3 Plugin vulnerability exploitation |
High |
Medium |
High |
Targets CoreDNS/ETCD plugins for code execution or poisoning. |
3.1.2.1 Privileged pod escape |
High |
Medium |
High |
Escape to node network namespace to intercept DNS flows. |
3.1.2.2 Node-level network access |
Medium |
Medium |
High |
DaemonSet/agent on nodes can observe/alter DNS traffic. |
3.1.2.3 Cross-namespace traffic interception |
High |
Medium |
Medium |
Exploits policy gaps for lateral DNS observation within cluster. |
3.1.3.1 Istio/Linkerd DNS redirection |
High |
Medium |
High |
Service mesh policies/proxies used to reroute DNS to attacker. |
3.1.3.2 mTLS certificate theft |
High |
High |
High |
Theft of mesh certs enables decryption/impersonation of DNS sidecars. |
3.1.3.3 Sidecar proxy manipulation |
Medium |
Medium |
High |
Misconfig or compromise of sidecars to tamper with DNS egress. |
3.2.1.1 Lambda TXT record exfiltration |
Medium |
Low |
Medium |
Uses serverless functions to encode data in DNS responses. |
3.2.1.2 Cloud Functions DNS over HTTPS |
Medium |
Low |
Medium |
DoH endpoints hosted on serverless for stealthy tunneling. |
3.2.1.3 Azure Functions private resolver abuse |
Medium |
Medium |
Medium |
Misusing private resolver integrations for data movement. |
3.2.2.1 DNS query burst attacks |
Low |
Low |
Medium |
Spiky invocation patterns drive resolver autoscaling/limits. |
3.2.2.2 Recursion depth exploitation |
Medium |
Low |
Medium |
Forces deep chains to inflate compute and latency. |
3.2.2.3 Cache saturation attacks |
Medium |
Medium |
Medium |
Fills caches with low-TTL entries to evict hot data. |
3.2.3.1 AWS Route 53 resolver hijacking |
High |
High |
High |
IAM/API misuse to change resolver rules/forwarders. |
3.2.3.2 Google Cloud DNS API abuse |
High |
High |
High |
Abuse of project/service accounts to alter DNS configuration. |
3.2.3.3 Azure Private DNS zone poisoning |
High |
High |
High |
Compromise of Azure DNS resources to redirect internal names. |
3.3.1.1 DNS spoofing for registry redirection |
Medium |
Medium |
High |
Manipulates name resolution to pull images from attacker infra. |
3.3.1.2 MITM attacks on image downloads |
High |
Medium |
High |
Intercepts registry traffic to inject malicious layers. |
3.3.1.3 Cache poisoning for malicious images |
Medium |
Medium |
High |
Seeds caching proxies with tampered image manifests. |
3.3.2.1 Malicious library injection via DNS |
High |
Medium |
High |
DNS redirection leading to malicious package sources. |
3.3.2.2 Dependency confusion attacks |
Medium |
Low |
High |
Public package names overshadow private ones using DNS hints. |
3.3.2.3 Package manager DNS hijacking |
High |
Medium |
High |
Alters resolver path for package indexes to attacker-controlled hosts. |
4.1.1.1 Cloudflare token theft |
Medium |
Medium |
High |
Stolen API tokens change DNS at registrar/hosted zones. |
4.1.1.2 AWS Route 53 key leakage |
High |
Medium |
High |
Exposed access keys enable authoritative DNS manipulation. |
4.1.1.3 Google Domains API abuse |
Medium |
Medium |
High |
Misused API credentials alter domain/records. |
4.1.2.1 Registrar support impersonation |
Low |
Low |
High |
Social engineering resets ownership or enables transfers. |
4.1.2.2 Post-GDPR WHOIS information gaps |
Low |
Low |
Medium |
Exploits limited contact visibility to facilitate impersonation. |
4.1.2.3 Phone number porting attacks |
Medium |
Low |
High |
SIM swap/port-out to intercept registrar 2FA challenges. |
4.1.3.1 EPP protocol exploitation |
High |
Medium |
High |
Targets registrar-registry channel; requires ecosystem access. |
4.1.3.2 Registry lock bypass |
High |
Medium |
High |
Circumvents lock controls through process gaps or insider help. |
4.1.3.3 Transfer process manipulation |
Medium |
Medium |
High |
Abuses transfer windows/auth codes to seize domains. |
4.2.1.1 CNAME mapping to unused cloud resources |
Low |
Low |
High |
Classic dangling CNAME takeover; easily automated discovery. |
4.2.1.2 NS record pointing to decommissioned servers |
Medium |
Low |
High |
Orphaned NS enables zone control for subdomains. |
4.2.1.3 MX record targeting disabled services |
Medium |
Low |
Medium |
Mail path hijack for phishing/data collection. |
4.2.2.1 GitHub Pages site cloning |
Low |
Low |
Medium |
Rebind subdomain to attacker’s pages for brand abuse. |
4.2.2.2 S3 bucket takeover |
Low |
Low |
High |
Recreates deleted buckets to host attacker content. |
4.2.2.3 Azure Blob Storage hijacking |
Low |
Low |
High |
Claims unbound storage names referenced by CNAMEs. |
4.2.3.1 SSL certificate procurement |
Medium |
Low |
High |
Validates control over taken subdomain to obtain certs. |
4.2.3.2 DNS record obfuscation |
Medium |
Low |
Medium |
Hides persistence with nested CNAMEs/TTL tricks. |
4.2.3.3 Monitoring evasion techniques |
Medium |
Low |
Medium |
Low-and-slow changes and selective responses to evade detection. |
4.3.1.1 Edge server cache poisoning |
High |
Medium |
High |
Poison CDN edge with malicious DNS/HTTP artifacts via control-path issues. |
4.3.1.2 Origin DNS spoofing |
High |
Medium |
High |
Redirect CDN to attacker “origin” by DNS manipulation. |
4.3.1.3 GeoDNS manipulation |
Medium |
Medium |
Medium |
Geographic split-horizon abuse to isolate victims. |
4.3.2.1 SAN certificate abuse |
High |
Medium |
High |
Misuses shared SAN certs for unintended hostnames. |
4.3.2.2 CDN SSL termination bypass |
High |
Medium |
High |
Forces traffic around expected TLS termination points. |
4.3.2.3 Multi-CDN configuration conflicts |
Medium |
Medium |
Medium |
Exploits inconsistent DNS/TLS between providers. |
5.1.1.1 DNS query pattern manipulation |
Medium |
Medium |
Medium |
Pollutes reputation systems with crafted benign-looking query mixes. |
5.1.1.2 Behavioral model contamination |
High |
Medium |
High |
Inserts poisoned samples into training/feedback loops. |
5.1.1.3 Feedback loop exploitation |
Medium |
Medium |
Medium |
Exploits automated block/allow updates to drift policies. |
5.1.2.1 GAN-generated benign-looking queries |
High |
Medium |
High |
ML-generated traffic mimics normal distributions to evade filters. |
5.1.2.2 CDN traffic mimicry |
Medium |
Low |
Medium |
Routes via popular CDNs to blend with noise and whitelists. |
5.1.2.3 Legitimate domain spoofing |
Medium |
Low |
Medium |
Uses typo/homograph names that resemble legitimate endpoints. |
5.1.3.1 Reinforcement learning for evasion |
Very High |
High |
High |
Trains agents to adapt queries against defenses; research-heavy. |
5.1.3.2 Genetic algorithm optimization |
High |
Medium |
Medium |
Evolves traffic features to reduce detection scores. |
5.1.3.3 Transfer learning across networks |
High |
Medium |
Medium |
Reuses models between environments to shorten tuning time. |
5.2.1.1 LLM-generated homograph domains |
Low |
Low |
High |
Automates convincing domain suggestions at scale. |
5.2.1.2 Context-aware typosquatting |
Low |
Low |
High |
Uses user/brand context to pick likely typos; increases click-through. |
5.2.1.3 Cultural adaptation algorithms |
Medium |
Low |
Medium |
Localizes domain choices to regions/languages. |
5.2.2.1 Dynamic DNS fast-flux networks |
Medium |
Medium |
High |
Rapidly changing DNS answers to resist takedown. |
5.2.2.2 Automated certificate procurement |
Low |
Low |
High |
Scripted DV issuance increases trust signals for phishing. |
5.2.2.3 Multi-CDN abuse for resilience |
Medium |
Medium |
Medium |
Spreads infrastructure across CDNs to survive blocking. |
5.2.3.1 NLP-based brand monitoring |
Medium |
Low |
Medium |
Identifies hot targets to register convincing domains. |
5.2.3.2 Social media sentiment analysis |
Medium |
Low |
Medium |
Times campaigns around trending events. |
5.2.3.3 Employee behavior prediction |
High |
Medium |
Medium |
Tailors lures using internal cadence/meeting patterns. |
5.3.1.1 AI-generated optimal attack timing |
High |
Medium |
Medium |
Uses forecasting to time bursts/downgrades for max effect. |
5.3.1.2 Neural network-based evasion patterns |
High |
Medium |
High |
Learns feature sets that current detectors overlook. |
5.3.1.3 Reinforcement learning for policy exploitation |
Very High |
High |
High |
Probes defenses to discover blind spots automatically. |
5.3.2.1 AI-assisted fuzz testing for BGPsec |
High |
Medium |
Medium |
(Cross-domain) automates fuzzing; limited direct impact on encrypted DNS. |
5.3.2.2 Machine learning for side-channel detection |
High |
Medium |
Medium |
Enhances side-channel signal extraction; still constrained by noise. |
5.3.2.3 Automated exploit generation |
Very High |
High |
Medium |
Early-stage capability; high setup cost. |
5.3.3.1 Self-modifying attack code |
High |
Medium |
High |
Polymorphic tunneling/backdoor code hampers signatures. |
5.3.3.2 Dynamic protocol manipulation |
High |
Medium |
High |
Switches among DoH/DoT/DoQ to evade static rules. |
5.3.3.3 Intelligent countermeasure evasion |
High |
Medium |
High |
Actively probes and adapts around rate limits and filters. |
6.1.1.1 ECDSA-P256 signature harvesting |
Low |
Low |
Medium |
Collects public DNSSEC signatures for potential future attacks. |
6.1.1.2 RSA-2048 key storage |
Low |
Low |
Medium |
Archives RSA signatures/keys (public) for harvest-now-decrypt-later strategies. |
6.1.1.3 NSEC3 chain enumeration |
Medium |
Low |
Medium |
Gathers structure of signed zones for later targeting. |
6.1.2.1 Long-term encrypted data storage |
Low |
Medium |
Medium |
Warehouses captured encrypted DNS/DoH traffic for future decryption. |
6.1.2.2 Future decryption capability planning |
Medium |
Medium |
Medium |
Organizes key materials and compute pipelines anticipating PQ-era. |
6.1.2.3 Harvest-then-decrypt campaigns |
Medium |
High |
High |
Strategic programs at scale to capture now and decrypt later. |
6.1.3.1 Algorithm confusion attacks |
High |
Medium |
Medium |
Exploits transition periods where multiple DNSSEC algs coexist. |
6.1.3.2 Hybrid scheme weaknesses |
High |
Medium |
Medium |
Attacks mis-implemented hybrid (classical+PQ) deployments. |
6.1.3.3 Backward compatibility exploitation |
Medium |
Medium |
Medium |
Forces fallback to pre-PQ algorithms/policies. |
6.2.1.1 Photon-splitting attacks |
Very High |
Very High |
Medium |
Specialised QKD attack; requires physical access and lab gear. |
6.2.1.2 Fake state attacks |
Very High |
Very High |
Medium |
Injects crafted quantum states to bias keys; niche, hardware-specific. |
6.2.1.3 Trojans in QKD hardware |
Very High |
Very High |
Medium |
Supply-chain/hardware implants in QKD components. |
6.2.2.1 Side-channel attacks on QKD systems |
Very High |
High |
Medium |
Exploits implementation leaks (detectors, timing). |
6.2.2.2 Laser intensity manipulation |
Very High |
High |
Medium |
Alters device behavior; difficult to mount covertly. |
6.2.2.3 Detector blinding attacks |
Very High |
High |
Medium |
Forces detectors into classical regimes; requires proximity. |
6.2.3.1 Classical-quantum interface exploitation |
High |
High |
Medium |
Targets key handoff between QKD and classical DNS/TLS systems. |
6.2.3.2 Key management system compromise |
High |
High |
High |
Compromise of KMS integrating PQ/QKD undermines entire chain. |
6.2.3.3 Quantum network routing attacks |
Very High |
Very High |
Medium |
Attacks on early quantum network control planes; emerging risk. |
7.1.1.1 Traditional DNS (TXT, NULL records) |
Low |
Low |
Medium |
Simple tunneling via classic records; widely detected by mature defenses. |
7.1.1.2 DoH/DoT/DoQ encrypted tunneling |
Medium |
Low |
High |
Encrypts payloads to bypass middleboxes; harder to monitor. |
7.1.1.3 ICMP-based DNS manipulation |
Medium |
Low |
Medium |
Covert data over ICMP with DNS semantics; niche and noisy. |
7.1.2.1 Query rate limiting bypass |
Medium |
Low |
Medium |
Distributes queries to evade per-source throttling. |
7.1.2.2 Legitimate traffic blending |
Medium |
Low |
High |
Shapes tunnels to match benign client/protocol patterns. |
7.1.2.3 Multiple resolver rotation |
Low |
Low |
Medium |
Rotates upstreams to evade IP-based detection. |
7.1.3.1 Base32/64 encoding variations |
Low |
Low |
Medium |
Obfuscates payloads within label constraints. |
7.1.3.2 Compression with error correction |
Medium |
Low |
Medium |
Balances throughput vs. reliability over lossy paths. |
7.1.3.3 Fragmentation and reassembly |
Medium |
Low |
Medium |
Splits payloads across queries to bypass size checks. |
7.2.1.1 Query response timing modulation |
Medium |
Low |
Medium |
Encodes bits in inter-arrival/latency; low bandwidth but stealthy. |
7.2.1.2 DNS refresh interval exploitation |
Medium |
Low |
Medium |
Uses refresh/probe timings as covert clock. |
7.2.1.3 TTL value manipulation |
Medium |
Low |
Medium |
Encodes data in TTL fields; detectable via anomalies. |
7.2.2.1 DNS cache poisoning with data |
High |
Medium |
High |
Seeds caches with attacker-controlled encodings for later retrieval. |
7.2.2.2 NSEC3 gap exploitation |
High |
Medium |
Medium |
Stores bits via crafted non-existent name patterns. |
7.2.2.3 DNSSEC signature embedding |
High |
Medium |
Medium |
Hides data in optional fields/signature slack; raises validation risks. |
7.2.3.1 Query sequence encoding |
Low |
Low |
Medium |
Uses order of labels/queries to represent data. |
7.2.3.2 Resolver selection patterns |
Low |
Low |
Medium |
Chooses resolvers in specific sequences as a codebook. |
7.2.3.3 Domain name generation algorithms |
Medium |
Low |
Medium |
DGA-based channels for resilient command/data paths. |
7.3.1.1 Dynamic domain generation |
Medium |
Medium |
High |
Rotates domains rapidly to evade blocklists. |
7.3.1.2 DNS-based payload delivery |
Medium |
Low |
Medium |
Delivers stage payloads via TXT/NULL to reduce HTTP exposure. |
7.3.1.3 Dead drop resolvers |
Medium |
Medium |
Medium |
Uses specific recursive resolvers as covert mailboxes. |
7.3.2.1 Distributed exfiltration aggregation |
Medium |
Medium |
Medium |
Fan-out/fan-in architecture to assemble data outside perimeter. |
7.3.2.2 On-the-fly decoding services |
Low |
Low |
Medium |
Cloud functions decode/forward tunneled chunks in real time. |
7.3.2.3 Cloud function data processing |
Medium |
Low |
Medium |
Serverless transforms/filters exfil data before storage. |
7.3.3.1 Multiple exfiltration pathways |
Medium |
Medium |
Medium |
Redundant DNS + alt channels improve resilience. |
7.3.3.2 Fallback communication channels |
Medium |
Low |
Medium |
Automatic switchover to new domains/resolvers/CDNs. |
7.3.3.3 Anti-forensic techniques |
High |
Medium |
High |
Deletes artifacts, pads timings, rotates keys to hinder IR. |
DNS heatmap¶
Attack Category | Example Attack Path | Risk Level | Likely Adversary |
---|---|---|---|
Exploit Protocol Weaknesses | TXID birthday poisoning, HTTP/2 HPACK, QUIC migration spoof | High | Nation-state / Skilled cybercriminal |
Attack Encrypted DNS | RST/GOAWAY/QUIC-close downgrades, ML traffic fingerprinting | High | Nation-state / ISP-level actor |
Cloud/SaaS Exploits | CoreDNS ConfigMap poisoning, Route 53 rule hijack | High | Cloud-savvy attacker / APT |
Supply Chain Attacks | Registrar API key theft, dangling CNAME takeover | High | Cybercriminal / APT |
AI/ML-Augmented Attacks | GAN-shaped queries, RL-based evasion | Medium | Well-resourced criminal / Research-grade actor |
Post-Quantum Threats | Harvest-now-decrypt-later, transition-period confusion | Medium | Nation-state / Strategic actor |
Data Exfiltration Techniques | DoH/DoT/DoQ tunneling, cache-based storage channels | High | Cybercriminal / APT |