IPv6 White Paper VI: Detection, Attribution, and Forensic Reconstruction of IPv6-Based Attacks Using Control-Plane Graphs

Abstract

As IPv6 adoption matures, attackers inevitably shift their attention toward the chatty and often unmonitored control-plane that governs configuration, reachability, neighbor discovery, and routing behavior. These attacks will rarely manifest as straightforward TCP or UDP anomalies. Instead, they blend into the underlying mechanisms that hosts depend on to function. Forged Neighbor Advertisements may poison caches, rogue Router Advertisements may redirect  traffic, and targeted creation or suppression of Duplicate Address Detection may block or impersonate legitimate hosts. These operations occur below the visibility of most enterprise defensive tooling. The challenge for defenders is not simply detecting that an attack has occurred, but reconstructing how it unfolded, identifying the actor or compromised device responsible, and deriving actionable forensic insights that can inform remediation. This requires a fundamentally different approach to attribution and reconstruction that is rooted not in packet logs or endpoint telemetry, but in long-term, behaviorally rich, control-plane graphs.

Diving Deeper

A passive IPv6 reconnaissance platform provides the foundation for such an approach. By continuously collecting and correlating control-plane events into a multi-layered graph of hosts, addresses, MAC bindings, router behaviors, multicast memberships, and temporal sequences, the platform builds a longitudinal fingerprint of normal network activity. It captures the cadence of Router Advertisements, the unique timing patterns of each host’s DAD cycles, the characteristic retransmission intervals of Neighbor Solicitations, and the stable structure of prefix lifetimes and routing preferences. These baselines form the behavioral framework against which anomalies can be identified and actioned. When an attack occurs, the traces appearing within this graph are not limited to malicious packets, but also as distortions in the expected structure, timing, and relationships of control-plane events. Detection, attribution, and forensic reconstruction become acts of graph analysis rather than pattern matching.

The detection phase begins when deviations from historical control-plane behavior break the structural coherence of the event graph. For example, a rogue router may appear as a new vertex in the topology layer, broadcasting prefixes or lifetimes inconsistent with the stable patterns established by legitimate routers. Its Router Advertisements may exhibit anomalous timing, option ordering, or identifier structure that does not align with known vendor profiles. A forged NA that attempts cache poisoning may create conflicting edges by binding an IPv6 address to a MAC address that lacks the expected DAD ancestry or privacy-rotation lineage. Suppression of DAD manifests as an increase in failed DAD cycles, each creating edges that violate the previously observed natural DAD-conflict rate of a stable environment. Each anomaly weakens portions of the graph’s internal consistency, triggering suspicion even when the packet-level behavior appears nominal.

While detection identifies irregularities, attribution requires determining which host or device generated the malicious control-plane events. Attackers often attempt to spoof source addresses, manipulate MAC identities, or inject crafted packets that resemble legitimate traffic. Purely syntactic analysis is insufficient as attribution relies on tying malicious events to long-term behavioral signatures unique to each device. An event-graph engine would enable this by correlating subtle characteristics such as retransmission timing, RA response delays, MLD membership habits, OS-specific option encoding, and even fluctuating patterns in DAD behavior. These patterns collectively form a behavioral fingerprint of each host which is far more difficult for an attacker to forge consistently than static identifiers such as IP or MAC addresses. If a rogue router advertisement emerges from a MAC address that has never historically produced routing traffic, but whose retransmission behavior and timing shifts match a previously observed endpoint, the platform can attribute the attack to a compromised host masquerading as infrastructure. Conversely, if a virtual machine instance begins emitting control-plane messages inconsistent with its baseline, there may be lateral movement by an attacker. Here the graph reveals a sudden behavioral divergence attached to an existing identity node, signaling compromise.

Forensic reconstruction builds upon attribution by piecing together the timeline and causal chain of events. Because the passive system timestamps every control-plane observation and stores it within a time-indexed graph, investigators can traverse the graph backward. This can be used to determine when anomalies first appeared, how they propagated, and which hosts reacted to the malicious behavior. For example, a rogue RA may have led multiple hosts to deprecate their preferred addresses prematurely. The graph would show these deprecations radiating outward in time from the attack vertex, allowing analysts to quantify the scope of impact. In a cache-poisoning attack, the system can trace which hosts issued NS queries that were answered incorrectly, how their neighbor caches changed, and whether any subsequent traffic shifts were detectable. In cases involving stealthy reconnaissance, such as an attacker replaying NS packets to map active addresses, the platform may reveal unnatural bursts of NS traffic lacking the contextual triggers that legitimate stacks generate, allowing analysts to reconstruct the reconnaissance sequence and correlate it with the rogue identity’s activity across the timeline.

One of the most powerful aspects of graph-based forensic reconstruction is its ability to reveal long-term attack dwell times that would otherwise remain invisible. A compromised host may gradually alter RA parameters, manipulate prefixes, or intermittently poison neighbor caches. These slow-burning attacks blend into normal traffic, but will inevitably accumulate inconsistencies over days or weeks. The event-graph engine preserves these inconsistencies, allowing investigators to trace them back to their earliest manifestation. Because control-plane graphs maintain historical integrity, they offer a forensically complete record of subtle abuse patterns that traditional logs often fail to capture. More-over these traditional logs are also likely to be ephemeral and volume-limited. The compact messaging of a control-plane graph in IPv6 is more conducive to long-term storage.

Another crucial capability is the graph’s ability to identify secondary effects and derived impact. IPv6 control-plane attacks often cascade: a poisoned neighbor cache may divert traffic through an attacker who then alters MLD memberships, which in turn may expose additional multicast services to exploitation. The graph naturally encodes these dependency chains, enabling deep reconstruction that extends beyond the initial attack vector. By following edges that represent indirect relationships such as multicast subscriptions triggered by bogus RA parameters or retransmissions triggered by suppressed DAD, the system can also reconstruct the full operational narrative of the attack, not just the root cause.

Attribution also benefits from the graph’s inference capability across multiple segments or observation points. If passive sensors exist across several L2 domains, a coordinated attack using rogue routers or forged ND traffic becomes traceable across segment boundaries. Behavioral fingerprints persist even when attackers change link-local addresses or move laterally across VLANs. The graph can therefore bind seemingly unrelated malicious events into a unified identity, revealing attackers’ movement patterns and operational sophistication.

Ultimately, detection, attribution, and forensic reconstruction in IPv6 control-plane analysis form a continuum of graph reasoning: 

  1. Detection identifies disruptions in structural and temporal stability. 
  2. Attribution maps disruptions to persistent identities based on behavioral fingerprints.
  3. Reconstruction reassembles the chronology and causal graph of the attack. 

This methodology transcends the limitations of packet capture and active scanning, providing defenders with a powerful forensic lens into attacks that exploit the implicit trust model of IPv6 protocol behavior.

Conclusion

As enterprise IPv6 deployments expand, adversaries will increasingly leverage the subtleties of the control-plane to achieve stealth. Only by interpreting that control-plane as a dynamic graph, rather than a sequence of isolated packets, can organizations attain the visibility necessary to detect subtle protocol abuse, attribute malicious actors with high confidence, and reconstruct attacks with full fidelity. The event-graph architecture described across this series provides the structural and analytical depth required to meet this challenge, and establishes a new foundation for IPv6-native defense, forensics, and threat hunting.

This article is part of a series of my work unlocking vulnerability scanning and network reconnaissance in IPv6 environments. To follow along the series, here is the rest of my writeups:

  1. IPv6 White Paper I: Primer to Passive Discovery and Topology Inference in IPv6 Networks Using Neighbor Discovery Protocol
  2. IPv6 White Paper II: Passive vs. Active IPv6 Scanning in Enterprise Reconnaissance Architectures
  3. IPv6 White Paper III: Architecture of a Passive IPv6 Reconnaissance Platform
  4. IPv6 White Paper IV: The Data Model, Correlation Pipeline, and Event-Graph Engine Behind Passive IPv6 Reconnaissance
  5. IPv6 White Paper V: Threat Detection and Rogue Activity Identification in IPv6 Control-Plane Traffic
  6. IPv6 White Paper VI: Detection, Attribution, and Forensic Reconstruction of IPv6-Based Attacks Using Control-Plane Graphs
  7. IPv6 White Paper VII: Scaling Passive IPv6 Reconnaissance Across Enterprise Fabrics and Multi-Site Deployments
  8. White Paper VIII: Building a Production-Grade IPv6 Discovery and Threat Intelligence Platform (End-to-End Engineering Blueprint)

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