Ukraine's Operation "Spider's Web" Redefines Asymmetric Warfare

1. Tu-9: A Soviet-era strategic bomber equipped with turboprop engines, used by Russia to launch long-range cruise missiles such as the Kh-55, Kh-555, and the newer Kh-101/102. Each aircraft can carry up to 16 cruise missiles. Despite its age, the Tu-95 remains a critical asset in Russia’s long-range strike capability.
    
2. Tu-22M3: A supersonic long-range bomber capable of carrying Kh-22 cruise missiles, which pose a severe challenge for Ukrainian air defenses due to their high speed. The Tu-22M3 forms part of Russia’s conventional and nuclear strike forces.
    
3. A-50: An AWACS aircraft used by Russia to detect air defense systems, coordinate missile strikes, and guide fighter aircraft. Russia has fewer than ten operational A-50s, and each is estimated to cost around $350 million. Their loss severely limits Russia’s situational awareness and air command capabilities.
    
4. Tu-160: A supersonic, variable-sweep wing strategic bomber and the largest combat aircraft in the world. Capable of carrying both nuclear and conventional cruise missiles, including the Kh-101 and Kh-102, the Tu-160 serves as a key component of Russia’s long-range strike and nuclear deterrent force.
    

The majority of aircraft confirmed damaged or destroyed belong to the core platforms used by Russia for strategic bombing and battlefield coordination.

Q3: How was the operation conducted?

A3: Planning for the operation reportedly began over 18 months prior to its execution. Ukrainian operatives smuggled around 150 small strike drones, modular launch systems, and 300 explosive payloads into Russia through covert logistical routes. The drones were concealed inside wooden modular cabins, which were then loaded onto standard cargo trucks.

An integral component of the operation was its use of covert logistics conducted through Russian territory, involving unwitting Russian civilian participants. As part of the operation’s deception strategy, the SSU reportedly recruited Russian truck drivers to deliver the mobile drone launchers camouflaged as standard cargo loads. These drivers were instructed to arrive at specific times and park at predesignated locations in the vicinity of Russian strategic air bases, including fuel stations and isolated roadside areas.

At the designated time, the roofs of the cabins were remotely opened, and the drones launched directly from within the trucks. This minimized the distance between launch and impact, allowing the drones to bypass Russia’s layered air defense systems—including Pantsir and S-300 units—before they could react. Notably, Russian sources confirmed the drones were launched from positions just outside the airfields, including from fuel stations and roadside laybys. After all the drones were launched, the trucks exploded, indicating that they were equipped with a self-destruction mechanism.

Altogether, 117 drones were launched, with over 40 aircraft struck, amounting to what Ukrainian sources estimate as 34 percent of Russia’s strategic cruise missile delivery platforms. This includes some of the few remaining A-50 airborne early warning and control aircraft, which are vital to Russia’s airspace surveillance and targeting operations.

Importantly, all personnel involved in the operation were successfully moved from Russian territory to Ukraine prior to drone launch. Ukrainian leadership, including President Zelensky and SSU chief Vasyl Maliuk, was reportedly closely involved in the planning and real-time coordination of the strike.

The success of Spider’s Web highlights a dramatic shift in the balance of initiative. Ukraine demonstrated the ability to execute a coordinated, multi-theater deep-strike operation, far beyond its borders, using fully indigenous systems and asymmetric tactics—blending deception, precision, and strategic surprise.

Q4: What role did AI play in Ukraine’s Spider’s Web drone operation?

A4: In Operation Spider’s Web, Ukraine demonstrated a hybrid approach to drone warfare that combined remote human control with elements of autonomy and potentially AI-assisted functionality. While the operation was not fully autonomous, the available evidence suggests that artificial intelligence likely played a supporting role in both flight stability and targeting, particularly in enabling precise strikes on vulnerable components of high-value aircraft.

The first-person-view (FPV) drones used in the operation were remotely controlled through Russian mobile telecommunications networks, including 4G and LTE connections. These networks provided sufficient bandwidth to support real-time video transmission and command inputs across vast distances, allowing Ukrainian operators to manage drone flights from outside Russian territory. This setup avoided the need for any physical ground control stations or nearby operators.

To enable stable long-distance control over mobile networks, the drones relied on a software-hardware system built around ArduPilot—a widely used, open-source autopilot framework designed for unmanned aerial vehicles. ArduPilot provides advanced flight stabilization, waypoint navigation, failsafe routines, and programmable mission profiles. In this case, each drone was integrated with a compact onboard computer (such as a Raspberry Pi), connected to a webcam and an LTE modem via Ethernet. The camera feed was used for visual navigation, while control signals were routed through ArduPilot’s UART interface, allowing operators to pilot the drone remotely with stabilized, responsive input—even when faced with significant signal latency.

ArduPilot’s flexibility makes it well-suited for missions operating over unstable or high-latency links, such as mobile internet, as it can independently manage the drone’s orientation, heading, and altitude, ensuring flight stability while awaiting operator commands. This made it the ideal choice for long-range, internet-based FPV control—especially when using improvised mobile launch platforms deep inside Russian territory.

In addition to manual control, AI-assisted targeting appears to have been integrated into the drones’ attack logic. According to open-source intelligence and reporting, SSU teams studied construction and visual profiles of the targeted aircraft—including Tu-95MS, Tu-22M3, and A-50 models, which are preserved in Ukrainian aviation museums like the Poltava Museum of Long-Range and Strategic Aviation—to identify precise weak points.

These profiles likely served as training data for machine vision models that were then embedded into the drones’ onboard computers. Such models could assist operators by identifying key structural weak points, such as underwing missile pylons and fuel tank seams, enabling rapid and precise final-stage maneuvering during the dive attack. The images released by the SSU confirm that the specific structural points, as shown in Figure 3, were identified as targets during the preparation phase, and later, official footage shows drones striking precisely at those designated areas.