When Your Toaster Joins a Botnet: ShadowV2’s World Tour

December 3, 2025

Synopsis

The analysis shows that ShadowV2 is a capable Mirai-based botnet that exploits known vulnerabilities in widely deployed IoT devices to expand a distributed DDoS infrastructure, using automated scanning, multi-CVE exploitation, and command-and-control mechanisms to weaponize unmanaged routers, DVRs, and NAS appliances across multiple global sectors. Strategically, the findings highlight the need for organizations to treat IoT exposure and DDoS resilience as board-level issues, integrate IoT asset governance into enterprise security policies, and reassess their reliance on cloud providers during outage windows that may coincide with botnet testing. Operationally and tactically, security teams must strengthen IoT inventory accuracy, patching workflows, segmentation, outbound traffic restrictions, and network-centric monitoring to detect CVE exploitation, loader delivery, C2 traffic, and unexpected high-volume outbound flows indicative of DDoS participation. Collectively, the widespread targeting of opportunistically exposed devices raises the organizational risk posture by increasing threat event frequency and susceptibility, especially in environments with weak IoT controls. Financial resilience is affected through the potential for service disruption, elevated incident response costs, reputational harm if internal devices participate in attacks on others, and secondary losses tied to customer impact or contractual obligations, reinforcing the need for proactive mitigation and continuous monitoring.

Evaluated Source, Context, and Claim

Artifact Title

ShadowV2 Casts a Shadow Over

https://www.fortinet.com/blog/threat-research/shadowv2-casts-a-shadow-over-iot-devices

Source Type

Vendor-backed cybersecurity research blog post (FortiGuard Labs)

Publication Date: November 26, 2025

Credibility Assessment

FortiGuard Labs is a well-established security research team within Fortinet that routinely publishes technical analyses of real-world threats, which generally lends it high credibility. As a vendor source, its findings are likely accurate on technical details but may reflect some vendor-centric perspective.

General Claim

The OSINT reports that a Mirai-based botnet variant named ShadowV2 is exploiting known vulnerabilities in widely deployed IoT devices across multiple countries and industries to build a DDoS-capable botnet, with recent activity during a global AWS outage assessed as a likely test run for future attacks.

Narrative Reconstruction

The information describes an unattributed threat actor operating a Mirai-based botnet variant called ShadowV2, which appears to be a moderately to highly capable, likely financially or service-motivated operator focused on Distributed Denial of Service. During a late-October global disruption of AWS connectivity, the actor used an infrastructure host at 198[.]199[.]72[.]27 to exploit multiple known vulnerabilities (including DDWRT CVE-2009-2765, D-Link CVE-2020-25506, CVE-2022-37055, CVE-2024-10914, CVE-2024-10915, DigiEver CVE-2023-52163, TBK CVE-2024-3721, and TP-Link CVE-2024-53375) in internet-facing IoT devices such as routers, DVRs, and NAS appliances, delivering a downloader script (binary.sh) that fetched the ShadowV2 malware from 81[.]88[.]18[.]108. Once installed, ShadowV2 initializes XOR-encoded configuration data, connects to a command-and-control domain (silverpath[.]shadowstresser[.]info, or the hardcoded IP fallback), and registers multiple DDoS attack methods over UDP, TCP, and HTTP, enabling it to receive commands and launch volumetric and application-layer floods. The targeted assets span IoT infrastructure in organizations across the Americas, Europe, Africa, Asia, and Oceania, covering the technology, retail and hospitality, manufacturing, managed security services, government, telecom, and education sectors, indicating that opportunistically exposed IoT devices, rather than specific named organizations, are the primary technical targets. Operationally, the campaign appears to focus on quietly expanding and testing a globally distributed DDoS botnet during an existing AWS outage window, likely to validate ShadowV2’s IoT-specific architecture and attack methods in preparation for more impactful service disruption or extortion-driven operations in the future.

Risk Scenario

Risk Scenario

A threat actor operating the Mirai-based ShadowV2 botnet exploits known vulnerabilities in our internet-facing IoT devices (such as routers, DVRs, or NAS appliances) to install malware that joins these devices to a remote-controlled DDoS botnet, resulting in service disruption to our online systems and potential collateral impact on customers who depend on our network availability.

Threat

A Mirai-lineage botnet operator, using the ShadowV2 malware, acts as an external, opportunistic but technically capable threat exploiting exposed IoT infrastructure worldwide, likely motivated by building and monetizing DDoS capacity or offering DDoS-as-a-service.

Method

The actor scans for vulnerable IoT devices and exploits published CVEs in platforms such as DD-WRT, D-Link, DigiEver, TBK, and TP-Link to deliver a downloader script (binary.sh) from 198[.]199[.]72[.]27, which then installs ShadowV2 from 81[.]88[.]18[.]108; infected devices establish C2 communications with silverpath[.]shadowstresser[.]info (or its fallback IP) and can be instructed to launch UDP, TCP, and HTTP DDoS floods against designated targets.

Asset

Primary assets at risk are any exposed or poorly segmented IoT devices in our environment that match or resemble the affected platforms (for example, internet-facing routers, DVRs, or NAS devices running vulnerable firmware), along with the dependent business services and customer-facing applications that rely on the availability and performance of the networks those devices sit on.

Impact

If ShadowV2 compromises our IoT devices and uses them in DDoS operations, victims face direct degradation or loss of availability for internal and external services, potential knock-on instability in thier network, increased operational and incident response costs, reputational harm if their infrastructure participates in attacks on others, and possible revenue or productivity loss when critical online services become slow or unreachable for customers or staff.

Evidentiary Basis for Synopsis and Recommendations

Supporting observations from the analysis help clarify how the threat landscape, control environment, and organizational behaviors interact to shape overall risk exposure. These insights provide the foundation for identifying where controls perform well, where gaps or weaknesses create unnecessary vulnerability, and how attacker methods intersect with real-world operational conditions. Building on these findings, the recommendations that follow focus on strengthening resilience, improving decision-making, and guiding readers toward practical steps that enhance both security posture and risk-informed governance.

FAIR Breakdown

Threat Event Frequency (TEF)

Because the OSINT describes a specific Mirai-based campaign that was globally active during a defined AWS outage window and is assessed as a likely test for future attacks, TEF must be inferred from ShadowV2’s scanning behavior, global footprint, and automation. For an organization running internet-exposed IoT devices similar to the listed platforms, TEF is likely moderate, as Mirai-lineage botnets typically perform persistent automated scanning, suggesting multiple contact attempts per year rather than rare, isolated events.

Contact Frequency (CF)

The campaign relies on automated exploitation attempts sourced from infrastructure like 198[.]199[.]72[.]27, scanning for specific CVEs across DD-WRT, D-Link, DigiEver, TBK, and TP-Link devices, which implies a steady, machine-driven CF rather than manual targeting. Sector targeting is broad and opportunistic—technology, retail and hospitality, manufacturing, MSSPs, government, telecom, and education — across multiple continents, indicating that any organization with vulnerable IoT gear on the internet-facing perimeter is likely to receive repeated probes over the course of a year.

Probably of Action (PoA)

ShadowV2 operators show clear motivation to grow a DDoS-capable botnet, as evidenced by their investment in multi-CVE exploitation, purpose-built IoT payloads, and sophisticated DDoS methods, indicating a high likelihood to action when a vulnerable device is found. The campaign’s global spread across many countries and industries, coupled with automated exploitation during a major AWS outage, suggests high campaign aggressiveness. Once contact with a vulnerable device is established, the actor is very likely to attempt a full compromise and enrollment in the botnet.

Threat Capability (TCap)

TCap is high, as ShadowV2 exhibits mature Mirai-lineage functionality, multi-platform exploit support, resilient C2 logic, and a rich DDoS capability set tailored to IoT environments.

Exploit sophistication: The actor chains internet-wide scanning with exploitation of published CVEs across multiple vendors (DD-WRT, D-Link, DigiEver, TBK, TP-Link), using a downloader script (binary.sh) to fetch and install the bot; this is technically moderate sophistication, but notable in its breadth of device and firmware coverage.

Bypass ability: By targeting IoT devices that often lack robust endpoint defenses and deep monitoring, and by using direct remote-exploitation vulnerabilities rather than social engineering, ShadowV2 effectively bypasses many traditional enterprise security controls focused on user endpoints and servers; fallback from domain-based C2 to a hardcoded IP further increases resilience against partial DNS-based defenses.

Tooling maturity: ShadowV2 reuses and extends the architecture of an existing Mirai variant (LZRD), with XOR-encoded configurations, structured attack-method tables, and support for multiple protocols (UDP, TCP, HTTP); this indicates high tooling maturity and a codebase that is designed for reuse and extension.

Campaign success rate: The OSINT reports incidents across many countries and multiple industries, implying that a non-trivial fraction of exposed, vulnerable IoT devices were successfully compromised; within the population of unpatched and internet-facing devices, the campaign success rate is likely moderate-to-high.

Attack path sophistication: The attack path—global scanning for specific IoT CVEs, remote exploitation from a centralized host, automated delivery of a downloader, retrieval of a tailored ShadowV2 binary, configuration decoding, C2 registration, and multi-method DDoS capability initialization—reflects a well-structured, multi-stage operation tailored to embedded devices.

Cost to run attack: Once the infrastructure, exploit modules, and bot binaries are in place, the marginal cost to continue scanning and exploitation is low; ShadowV2 is therefore inexpensive to operate at scale, making continued and repeated campaigns economically feasible for the actor.

Control Strength (CS)

Typical enterprise control strength against this class of IoT botnet is mixed, with relatively weak preventive controls on legacy or unmanaged IoT devices but potentially stronger network-level detection and blocking in more mature environments.

Resistive Strength (RS) Effectiveness of preventive/detective controls:

• Many organizations lack robust IoT firmware patching and configuration hardening, reducing their preventive strength against known CVEs listed in OSINT.

• Network-based IPS, firewalls, and anomaly detection may detect or block some ShadowV2 exploit attempts or outbound C2 traffic.

• Logging and monitoring on IoT devices are often limited, decreasing the likelihood of early detection.

• Segmentation and ACLs may exist in some environments, but are inconsistently applied across IoT device classes.

Control Failure Rate

• IoT devices are frequently unmanaged or poorly inventoried, resulting in unmonitored exposure.

• Firmware patching is often delayed or absent, leaving exploitable CVEs unaddressed.

• Weak or default configurations increase the likelihood of compromise upon contact.

• Lack of security telemetry on embedded devices contributes to delayed or failed detection.

• Inconsistent enforcement of segmentation controls allows compromised devices broader internal access.

Susceptibility

Given the high threat capability, broad and automated targeting, and only moderate typical control strength for IoT, overall susceptibility for an organization with exposed, vulnerable IoT devices is estimated at approximately 50–65 percent, representing a meaningful likelihood that assets will be harmed if contacted by ShadowV2.

Exploitability: For devices running the listed vulnerable firmware versions and exposed to the internet, exploitability is high (estimated 70–85 percent) because the attack path relies on known CVEs with working exploits, not complex zero-days.

Attack surface: Organizations with multiple IoT devices (routers, DVRs, NAS, cameras) on or near the perimeter may have 30–60 percent of those devices exposed or insufficiently segmented, increasing the practical attack surface.

Exposure conditions: When IoT devices are directly reachable from the internet or from semi-trusted networks without strong ACLs, exposure conditions push susceptibility higher (for those devices) into the 60–80 percent range.

Patch status: Where firmware is outdated, or vendor patches are lagging, patch-related mitigation can be minimal (0–20 percent risk reduction), meaning patch status often does little to counter the botnet’s exploitation capability unless a disciplined IoT patching program is in place.

Numerical Frequencies and Magnitudes

All values relating to actual dollar amounts are for example/speculative purposes only. Organizations would need to take into account their own asset values, control strength, telemetry, etc., and adjust numbers accordingly.

Loss Event Frequency (LEF)

5/year (estimated)

• Justification: For an organization with one or more internet-exposed, vulnerable IoT devices, globally active Mirai-lineage botnets like ShadowV2 can reasonably be expected to make several exploitation attempts per year as part of ongoing automated scanning activity.

Vulnerability (probability of harm per contact): .5

• Justification: Assuming moderate IoT control maturity, approximately half of contact attempts on truly vulnerable and exposed devices may result in successful compromise, given known-CVE exploitation, weak device hardening, and limited monitoring in many environments.

Secondary Loss Event Frequency

0.8/year (estimated)

• Justification: Not every successful IoT compromise will escalate into a noticeable business-impacting event (for example, some infections may be contained or have minimal operational effect), but a meaningful fraction—assumed here at roughly one-third of primary events—can produce secondary impacts such as reputational damage, customer concern, or third-party complaints if the compromised devices participate in visible DDoS activity.

Loss Magnitude

Estimated range:

• Min: $5,000

• Most Likely: $40,000

• Maximum: $250,000

Justification:

• The minimum case covers device triage and replacement, localized network remediation, and limited incident-response labor when botnet activity is quickly detected and contained.

• The most likely case assumes broader investigation, downtime or performance degradation for affected services, coordinated patching or replacement of multiple IoT devices, and internal IR/engineering time, driving costs into the low five figures.

• The maximum case reflects a scenario in which ShadowV2-driven DDoS participation or local resource exhaustion causes significant service interruption to critical business systems, resulting in an extended outage, extensive response efforts, and potentially short-term revenue or productivity losses.

Secondary Loss Magnitude (SLM)

Estimated range:

• Min: $20,000

• Most Likely: $150,000

• Maximum: $750,000

Justification:

• Secondary losses may include reputational harm if the organization’s infrastructure is implicated in DDoS attacks, customer or partner remediation efforts, additional security assessments, and possibly contractual or regulatory scrutiny if availability commitments are affected.

• The minimum reflects limited reputational and communication costs plus some extra security work; the most likely value assumes more pronounced customer impact and follow-on assessments; the maximum bounds a high-impact scenario involving major service disruption, reputational damage, and extensive external communication and remediation activities driven by ShadowV2-facilitated DDoS operations.

Mapping, Controls, and Modeling

MITRE ATT&CK Mapping

Reconnaissance

T1595 – Active Scanning

Reference: “Fortinet sensors detected active exploitation attempts… This variant was propagating through multiple vulnerabilities identified and blocked by our Intrusion Prevention System (IPS).”

Initial Access

T1190 – Exploit Public-Facing Application

Reference: “The attacker spreads a downloader script binary.sh by exploiting multiple vulnerabilities… leveraging vulnerabilities affecting the following vendors’ products.”

Reference: Listed CVEs for DD-WRT, D-Link, DigiEver, TBK, TP-Link.

Execution

T1059 – Command Execution (OS-level execution through dropped script)

Reference: “The attacker spreads a downloader script binary.sh… and delivers the ‘ShadowV2’ malware.”

T1204 – User Execution (Malware Execution)

Reference: “ShadowV2… initializes its XOR-encoded configuration and its attack methods,” indicating execution of the delivered binary.

Defensive Evasion

T1027 – Obfuscated/Encoded Files or Information

Reference: “It XOR-decodes its configurations using a single-byte key, 0x22.”

Command and Control

T1071 – Application Layer Protocol (HTTP)

Reference: “It connects to a C2 server to receive commands that trigger DDoS attacks.”

T1095 – Non-Application Layer Protocol (TCP/UDP fallback C2)

Reference: “If the domain cannot be resolved… ShadowV2 falls back to directly connecting to the hardcoded C2 server IP address.”

T1568 – Dynamic Resolution

Reference: “ShadowV2 first attempts to resolve C2 server domain silverpath[.]shadowstresser[.]info.”

Impact

T1499 – Endpoint Denial of Service

Reference: “ShadowV2… triggers DDoS attacks using the corresponding attack method ID and parameters.”

T1498 – Network Denial of Service

Reference: “Implemented attack methods including UDP floods, several TCP-based floods, and HTTP-level floods.”

NIST 800-53 Affected Controls

RA-5 — Vulnerability Monitoring and Scanning

Reference: “ShadowV2… leveraging vulnerabilities affecting the following vendors’ products… CVE-2009-2765, CVE-2020-25506, CVE-2022-37055, CVE-2024-10914…”

Mapping Justification: Exploitation of unpatched CVEs directly attacks an organization’s failure to identify and remediate known vulnerabilities.

SI-2 — Flaw Remediation

Reference: “It leveraged vulnerabilities affecting… DD-WRT, D-Link, DigiEver, TBK, TP-Link devices.”

Mapping Justification: Outdated firmware and unremediated flaws permit direct compromise.

CM-7 — Least Functionality

Reference: “IoT devices remain a weak link… enforcement of robust security practices is critical.”

Mapping Justification: IoT devices often expose unnecessary services and default configurations, making them vulnerable to successful exploitation.

AC-4 — Information Flow Enforcement

Reference: “The attacker spreads a downloader script… delivering the ShadowV2 malware from 81[.]88[.]18[.]108.”

Mapping Justification: Inadequate network flow restrictions allow inbound exploit attempts and outbound malware retrieval.

SC-7 — Boundary Protection

Reference: "Fortinet sensors detected active exploitation attempts… propagating through multiple vulnerabilities.”

Mapping Justification: The exploitation of internet-facing IoT devices indicates that boundary protections were insufficient or misconfigured.

SC-7(3) — Boundary Protection | Deny Communications Traffic by Default

Reference: “It leveraged vulnerabilities… connecting to the C2 server… fallback to the hardcoded IP address.”

Mapping Justification: Devices allowed outbound traffic to untrusted C2 infrastructure, bypassing expected outbound restrictions.

SC-5 — Denial-of-Service Protection

Reference: “ShadowV2… triggers DDoS attacks… UDP floods, TCP-based floods, HTTP-level floods.”

Mapping Justification: Malware enables the compromised device to participate in DDoS attacks, violating protections meant to limit DoS propagation.

SI-4 — System Monitoring

Reference: “Fortinet sensors detected active exploitation attempts linked to ShadowV2.”

Mapping Justification: Detection occurred only at the vendor’s IPS, implying gaps in internal monitoring for exploit activity or anomalous outbound C2 traffic.

SI-3 — Malicious Code Protection

Reference: “The attacker spreads a downloader script binary.sh… delivers the ‘ShadowV2’ malware.”

Mapping Justification: Malware installation indicates gaps in anti-malware controls on IoT devices, which often lack native protections.

PL-8 / PM-5 — System Inventory and IoT Asset Tracking (mapped to NIST via IoT device inventory guidance)

Reference: “Affected platforms: DD-WRT… D-Link… DigiEver… TBK… TP-Link… Any organization.”

Mapping Justification: Organizations frequently lack an accurate inventory of embedded/IoT devices, leading to unknown exposure to ShadowV2-compatible exploits.

Monitoring, Hunting, Response, and Reversing

Monitoring

Monitoring should prioritize high-fidelity network telemetry (firewall, IDS/IPS, NetFlow, packet capture) on internet-facing segments hosting DD-WRT, D-Link, DigiEver, TBK, and TP-Link devices, with DNS logging for lookups to silverpath[.]shadowstresser[.]info and connections to 198[.]199[.]72[.]27 and 81[.]88[.]18[.]108, plus whatever syslog or API logging the IoT platforms can expose (admin logins, configuration changes, firmware version, interface status). Logging levels on perimeter devices and any IoT management gateways should be raised to capture exploit alerts for the listed CVEs, HTTP requests that deliver or retrieve binary.sh and ShadowV2, and high-volume outbound UDP/TCP/HTTP flows resembling DDoS traffic from IoT addresses. At the same time, cloud and identity logs primarily help confirm whether IoT management interfaces were abused rather than serving as the primary signal. Key indicators to prioritize include repeated exploit attempts from 198[.]199[.]72[.]27, HTTP/S transfers from 81[.]88[.]18[.]108, DNS or direct connections to silverpath[.]shadowstresser[.]info or its resolved IP, new or sustained high-rate UDP/TCP/HTTP flows from IoT devices, and any sudden change in device behavior during major internet events such as large cloud outages. Monitoring gaps exposed by this threat include weak or absent IoT logging, lack of inventory-driven views of vulnerable firmware versions, and limited DDoS participation visibility (e.g., no per-device flow dashboards), so correlation logic should tie together CVE exploitation alerts, subsequent outbound retrieval of ShadowV2, C2 connectivity, and abnormal outbound traffic rates per device, with alert thresholds tuned for “any exploit attempt plus C2 contact” and “sustained DDoS-like throughput” rather than raw scan counts alone. Recommended dashboard updates include an IoT exposure panel showing counts of affected device models and firmware, a C2 communication panel keyed on the ShadowV2 infrastructure, and time series of outbound volumetric traffic by IoT subnet, and monitoring validation should use safe, simulated exploit and C2 patterns (e.g., test signatures, traffic replays, lab devices) to confirm that sensors, correlation rules, and visualizations actually trigger and present ShadowV2-like activity as intended.

Hunting

Hunting should revolve around hypotheses such as “one or more IoT devices in our environment have been probed or exploited via the ShadowV2 CVEs,” “an IoT device retrieved binary.sh or ShadowV2 from 81[.]88[.]18[.]108,” or “our IoT addresses are beaconing to or participating in attacks coordinated by silverpath[.]shadowstresser[.]info.” Network telemetry (IPS logs, firewall logs, NetFlow, packet capture) and DNS logs are primary sources, supplemented by any available device logs from routers, DVRs, or NAS appliances that match the affected platforms, and cloud/identity data is used mainly to track administrative access to IoT management interfaces. Detection logic should include hunts for exploit signatures on the specific CVEs tied to DD-WRT, D-Link, DigiEver, TBK, and TP-Link, flows from internal IoT addresses to 198[.]199[.]72[.]27 and 81[.]88[.]18[.]108, DNS queries for silverpath[.]shadowstresser[.]info, and patterns of outbound UDP/TCP/HTTP traffic consistent with DDoS from devices that typically produce low, steady traffic. Noise-to-signal considerations are important: exploit scans and background internet-wide scanning will generate plenty of hits, so hunters should prioritize combinations of signals (e.g., exploit attempt plus outbound file retrieval plus C2 connection from the same device) and focus on devices using known vulnerable firmware versions, thereby filtering out benign scanning noise and concentrating on activity chains that match the ShadowV2 kill chain.

Response

Response should be planned to quickly gather network logs (firewall, IDS/IPS, NetFlow, packet captures) showing exploit attempts from 198[.]199[.]72[.]27, HTTP/S transfers from 81[.]88[.]18[.]108, and C2 traffic to silverpath[.]shadowstresser[.]info, along with any available IoT device logs and configuration exports to confirm firmware versions, uptime, and configuration changes during the suspected compromise window. Expected artifacts include records of CVE-specific exploit signatures, HTTP requests for binary.sh and ShadowV2 binaries, DNS resolutions and subsequent TCP/UDP sessions to the C2 domain or IP, and captured DDoS traffic patterns originating from IoT addresses, plus any recovered binaries or memory images from lab-recreated infections if field devices lack forensics support. The OSINT does not describe explicit anti-forensic behavior. Still, practical IR gaps will stem from limited storage, minimal logging, and scarce endpoint tooling on embedded devices, so event reconstruction will rely heavily on network evidence to sequence the exploit, followed by download, then C2 registration, then DDoS operations. DFIR outputs, such as the duration of malicious traffic, the number and type of affected devices, and service degradation intervals, should be fed back into FAIR analysis to refine frequency and loss-magnitude estimates beyond generic ranges. Likely containment steps include blocking the ShadowV2 infrastructure (198[.]199[.]72[.]27, 81[.]88[.]18[.]108, silverpath[.]shadowstresser[.]info), isolating or rate-limiting IoT subnets, factory-resetting or reimaging affected devices, and rapidly upgrading firmware that addresses the listed CVEs; priority artifacts are any captured ShadowV2 binaries and binary.sh scripts, network captures of exploit and DDoS traffic, and device inventories that pinpoint exposed models and firmware. Telemetry requirements and IR gaps should be documented as follow-on work items (such as enabling IoT syslog export, adding SPAN ports for critical IoT segments, or deploying specialized monitoring), and DFIR validation strategies should include re-running safe lab reproductions of the infection chain to confirm that new controls prevent download and C2 registration while still allowing responders to detect and investigate the simulated activity.

Reverse Engineering

Reverse engineering should focus first on reproducing the loader behavior described in the OSINT: observing how binary.sh, retrieved from infrastructure like 198[.]199[.]72[.]27, downloads and executes ShadowV2 from 81[.]88[.]18[.]108 on representative DD-WRT, D-Link, DigiEver, TBK, or TP-Link firmware in a controlled lab, and documenting each network and process step. Analysts should examine ShadowV2’s XOR-encoded configuration (decoded with key 0x22) to catalog internal file paths, HTTP headers, User-Agent strings, and any other static identifiers, then map how the malware initializes its attack-function table and DDoS methods for UDP, TCP, and HTTP, and how it performs DNS resolution of silverpath[.]shadowstresser[.]info before falling back to the hardcoded IP. Evasion analysis should capture how simple encoding of the configuration and flexible C2 resolution (domain plus direct IP) complicate static detection and simple DNS-based blocking, even though no advanced stealth techniques are described, and persistence behavior should be explicitly confirmed or refuted in lab systems since the OSINT does not specify any mechanism beyond runtime execution. Indicators to extract include binary names (such as shadow.x86_64), specific config strings, the C2 domain and IPs, loader script names and URIs, and clear protocol and packet patterns for each DDoS method, with dynamic hooks placed on network APIs, config-decode routines, and the dispatch logic that triggers individual attack methods. Expected artifacts from reverse engineering include fully decoded configuration files, function maps of the DDoS routines, signatures for static and behavioral detection, and traffic captures for each attack mode. Additional suggestions include building YARA and Suricata/IDS rules from the decoded strings, tracking future ShadowV2 variants for changes to configuration encoding or C2 infrastructure, and sharing structured findings with CTI and detection engineering teams to keep signatures aligned with observed evolution.

CTI

From a CTI perspective, PIRs should ensure continuous assessment of whether ShadowV2 targets our sector, geography, or partners by comparing our IoT footprint against affected vendors and noting overlaps with technology, retail and hospitality, manufacturing, MSSPs, government, telecom, or education environments. Analysts should track the recurrence of ShadowV2 across its earlier AWS EC2 campaign and the later IoT-focused test during the AWS outage, monitor consistent TTPs such as repeated IoT CVE exploitation, and reuse of binary.sh, XOR-encoded configurations, the silverpath[.]shadowstresser[.]info infrastructure and fallback IP, and its UDP/TCP/HTTP DDoS methods, and prioritize assets such as internet-facing IoT devices and partner environments hosting similar routers, DVRs, or NAS appliances. SIR work should capture missing IOCs, expand malware sampling, and clarify infrastructure relationships to strengthen confidence in attribution. At the same time, collection should combine OSINT monitoring, sandboxed sample harvesting, internal telemetry correlation, and ISAC/ISAO collaboration, supplemented by targeted underground monitoring for ShadowV2-associated terms. Mapping and clustering should align ShadowV2 activity to MITRE ATT&CK, compare it with historical Mirai and LZRD campaigns, and evaluate whether observed patterns indicate incremental evolution or a strategic shift toward IoT-heavy DDoS operations, maintaining explicit confidence levels and revisiting the “test run” hypothesis as new telemetry or OSINT reveals changes in targeting, device families, campaign aggressiveness, or infrastructure maturity.

GRC and Testing

Governance

Governance should ensure that policies explicitly cover IoT security, third-party device management, and DDoS resilience, with transparent patching, segmentation, and exposure requirements for routers, DVRs, NAS appliances, and similar embedded devices that match the affected ShadowV2 platforms. Oversight functions (risk committee, security steering group, or equivalent) should regularly review metrics on internet-facing IoT inventory, firmware currency for the listed CVEs, and participation in DDoS activity, making IoT exposure a recurring agenda item alongside more traditional endpoints and cloud. RA, PM, and PL family documents should be updated to include IoT-specific risk analysis, explicit treatment of Mirai-style botnet threats, and named responsibility for maintaining accurate IoT asset inventories, applying firmware patches, and enforcing segmentation and outbound restrictions on these devices. The risk register should add or update a ShadowV2-style scenario that describes the compromise of IoT infrastructure and the resulting DDoS-related availability losses, with FAIR-informed frequency and magnitude ranges, and clearly defined risk owners and planned treatments. Board and executive communication should translate this into business terms by highlighting how unmanaged IoT devices can silently become part of a botnet that degrades revenue-generating services or harms customers, summarizing current exposure, control maturity, remediation timelines for the listed CVEs, and any dependency on critical cloud providers whose outages may coincide with botnet testing.

Audit and Offensive Security Testing

Audit and offensive security testing should prioritize verifying whether existing controls actually address the IoT botnet risk highlighted by ShadowV2, rather than assuming server-centric or user-centric controls are sufficient. Internal and external audits should look for evidence of comprehensive IoT inventories, documented patch and configuration baselines for the affected device families, and monitoring of outbound connections to known malicious infrastructure; gaps such as unknown IoT devices, undocumented firmware versions, or missing DDoS visibility should be treated as findings that increase susceptibility to ShadowV2-like campaigns. Policies and controls around boundary protection, flaw remediation, and monitoring should be validated by red and purple team exercises that explicitly attempt to exploit the ShadowV2 CVEs (in a lab or tightly controlled staging environment), retrieve a benign stand-in payload from ShadowV2 infrastructure look-alikes, and test whether C2-style connections and DDoS-like traffic from IoT segments are detected and contained. Penetration testing scopes should be expanded to include IoT segments, default router/DVR/NAS configurations, and outbound traffic controls, and exploit reproduction should simulate the observed chain (CVE exploitation, downloader script, IoT-focused payload, C2 registration, multi-protocol DDoS) using safe tooling to test whether compensating controls (IPS signatures, ACLs, rate limits, segmentation) fire as designed. Control validation should be documented as part of assurance reporting to governance and risk owners, linking each successfully or unsuccessfully blocked step in the ShadowV2 kill chain back to specific NIST 800-53 controls and FAIR scenario assumptions.

Awareness Training

Awareness training in this scenario should focus less on classic email phishing and more on the human failure modes that allow insecure IoT deployment and management to persist, since ShadowV2 relies on technical exploitation of devices rather than social engineering of end users. Role-specific training for network and system administrators should stress the business consequences of leaving internet-facing IoT devices unpatched, unsegmented, or running vulnerable firmware, using the ShadowV2 scenario as a concrete example of how “appliance” devices can be weaponized for DDoS and degrade availability for customers. Executives and business owners should receive brief, non-technical training on recognizing the risk signals of unmanaged IoT growth (shadow purchases, unmanaged cameras or DVRs, low-cost routers in remote offices) and on asking the right questions about inventory, patch status, and DDoS readiness, while customer-facing teams and operations staff should be trained to recognize symptoms of availability attacks (intermittent slowness, customer complaints, upstream provider issues) and the importance of quickly routing such observations to the appropriate technical teams. Simulation and measurement can focus on tabletop exercises and scenario walk-throughs rather than phishing tests, evaluating how well administrators, operations, and leadership respond to a scripted ShadowV2-style IoT compromise and how quickly they can identify affected devices, apply patches, and communicate impacts, with periodic reinforcement cycles aligned to significant infrastructure changes or new IoT deployments.