In the rapidly shifting landscape of cyber warfare, few professionals possess the multi-disciplinary perspective of Dominic Jainy. With a career forged at the intersection of artificial intelligence, machine learning, and blockchain, Jainy has become a leading authority on how emerging technologies are weaponized by state-linked actors. Today, as we navigate the complexities of 2026, his insights into the fusion of legitimate communication platforms and sophisticated surveillance have never been more critical. We are sitting down to discuss a particularly insidious threat known as HEAVYGRAM—a Windows-based surveillance backdoor that has fundamentally altered our understanding of command-and-control infrastructure. By repurposing the Telegram API, attackers have moved away from traditional, easily blocked servers to a model that hides in plain sight, blending malicious instructions with the daily digital noise of millions.
Our conversation dives deep into the architecture of these “modern surveillance centers,” exploring the tactical shift from dedicated servers to bot-driven ecosystems. We examine the psychological precision of the lures used against high-value targets, such as Iranian dissidents and international journalists, and break down the technical persistence mechanisms that allow this malware to survive even the most rigorous reboots. Jainy offers a granular look at the HEAVYGRAM kill chain, the role of secondary payloads like CRUDEEXCLUDE, and the broader geopolitical motivations that link these operations to the Handala Hack group. Beyond the technical mechanics, we discuss the practical realities for organizations and individuals at risk, providing a strategic roadmap for defense in an era where the tools we trust are the very ones being turned against us.
How have you seen the shift in attacker behavior as they transition from dedicated command-and-control servers to leveraging mainstream platforms like the Telegram API for managing HEAVYGRAM?
The transition we are witnessing represents a profound evolution in how threat actors view “hiding in plain sight.” In the past, a dedicated command-and-control server acted like a lighthouse for defenders; once you found the IP address, you could cut off the signal and isolate the infection fairly quickly. However, by turning Telegram into an operational command center, the HEAVYGRAM operators have essentially integrated their malicious traffic into a stream of data that looks identical to routine web activity. I’ve watched as this malware uses Telegram bots and accounts to receive instructions and exfiltrate data, which makes it incredibly difficult for standard perimeter defenses to flag. It creates a sense of digital camouflage; when a Windows machine checks in with a bot, it doesn’t look like a breach—it looks like a user checking their messages. This methodology has been refined since the fall of 2023, and it has proven devastatingly effective against journalists and dissidents who rely on these very platforms for their daily work.
When we look at the internal architecture of HEAVYGRAM, what specific mechanisms allow it to maintain such a deep, persistent foothold within a Windows environment?
HEAVYGRAM is designed for the long haul, and its persistence strategy is both surgical and aggressive. Once it secures that initial foothold, it immediately begins weaving itself into the Windows registry to ensure it survives every system restart. We have identified 29 additional samples, loaders, and payloads that work in tandem to keep this surveillance window open for as long as possible. One of the most clever aspects is the “health message” system—every 24 hours, the infected device sends a beacon back to the operators, letting them know the prey is still active and the line is still open. It’s a chillingly patient approach to espionage, where the goal isn’t just a quick hit, but a sustained presence that allows for the capture of cached information and the continuous theft of Telegram desktop data. The malware can even trigger secondary payloads or delete files to cover its tracks, making the infected machine feel less like a tool and more like a remote-controlled asset for a foreign intelligence service.
The social engineering aspect here seems particularly refined, using decoys like KeePass or Pictory. How do these lures exploit human trust to initiate the infection?
The brilliance, if you can call it that, of the HEAVYGRAM campaign lies in its contextual empathy. The attackers don’t just send random files; they approach victims through messaging apps while posing as familiar contacts or technical support, using persuasive decoys that mirror the target’s professional life. Imagine a journalist receiving a file that looks like a routine application update or a secure password manager like KeePass—there is an inherent level of trust already built into those interactions. We’ve seen first-stage files disguised as legitimate programs with names that are visually indistinguishable from the real thing, which bypasses the natural skepticism of even tech-savvy users. When the victim clicks that file, they might see a decoy document or a video that serves as a sensory distraction, while in the background, the malware is silently unpacking archives and establishing its persistence. It’s a classic shell game where the victim is looking at a “Pictory” installation screen while their system is actually being handed over to a remote operator.
Regarding the “CRUDEEXCLUDE” samples identified in recent research, how does this secondary layer of the attack prepare the ground for the main surveillance payload?
CRUDEEXCLUDE acts as the vanguard for the entire operation, performing the dirty work of clearing a path through the target’s local defenses. Its primary function is to add specific security-exclusion paths to the Windows environment before the actual HEAVYGRAM implant is even released. By doing this, the attackers significantly reduce the chance of detection by antivirus software or other endpoint protection tools that might otherwise flag the surveillance behavior. It is a very deliberate, two-step dance: first, you blind the guards with CRUDEEXCLUDE, and then you walk through the front door with HEAVYGRAM. This combination allows the attackers to execute DLL side-loading, where a perfectly legitimate program is tricked into loading a harmful companion file. It’s a sophisticated way to piggyback on trusted processes, ensuring that the malware’s activities—like capturing audio or running arbitrary commands—are masked by the “clean” reputation of the host application.
The research points toward a connection with the Handala Hack group and Iranian interests. What does this tell us about the evolving nature of state-sponsored surveillance in 2026?
The link to Handala Hack, which researchers have established with moderate confidence, suggests that we are looking at a surveillance effort that is deeply integrated with broader geopolitical goals. This isn’t just about stealing data; it’s about coercive activity and long-term access to the private communications of people whose views oppose a specific government. When you look at the victims—ranging from a UK-based Farsi-language journalist to dissidents in the U.S.—the pattern of targeting becomes clear. This operation reflects a shift toward using destructive intrusions and surveillance as a unified tool of statecraft, often linked back to entities like the MOIS. In 2026, the lines between criminal hacking and national intelligence operations have blurred entirely, and groups are now using these “community” platforms like Telegram because they provide a global, resilient, and low-cost infrastructure for high-stakes espionage. It’s a sobering reminder that the digital battleground is often found in the most mundane corners of our personal devices.
For an organization or a high-risk individual, what does a truly effective defense look like when the malicious traffic is essentially indistinguishable from routine web activity?
Effective defense now requires a “zero-trust” mindset even for the most routine communications. For organizations, it starts with the granular monitoring of outbound bot API connections; if Telegram isn’t an approved business tool, its traffic should be blocked or, at the very least, intensely scrutinized. We’ve seen that identifying unusual native-process launches or system folders with trailing spaces can be the key to catching an infection early. On a more practical level, teams that integrate high-fidelity indicators of compromise into their workflows can cut every SOC alert investigation by as much as 21 minutes, which is an eternity when you’re trying to stop data exfiltration in real-time. For individuals, the advice is more personal: you must verify unexpected contacts through separate, trusted channels and be incredibly cautious with any file that arrives unrequested, even if it seems to come from a colleague. Restricting messaging-app privacy settings and ensuring that every binary launch is restricted from user-writable locations like APPDATA or ProgramData are no longer optional steps; they are essential survival skills in this environment.
What is your forecast for the future of API-based malware?
My forecast is that we will see a massive surge in “legitimate-service-as-a-C2” models, where malware moves beyond Telegram to exploit the APIs of nearly every major collaborative platform, from Slack to Microsoft Teams. As encryption becomes more ubiquitous and harder for traditional security tools to inspect, attackers will increasingly hide their control signals inside the encrypted tunnels of these trusted services. We are likely to see the rise of autonomous malware agents that use machine learning to mimic a user’s specific typing patterns and communication style within these APIs, making the “social engineering” phase completely automated and nearly impossible to distinguish from a real person. By 2028, the battle won’t just be about blocking bad IPs; it will be about the deep, behavioral analysis of API calls and the constant verification of digital identity at every single interaction point. We are moving toward a reality where the platform itself is neutral, and the only way to stay safe is to monitor the intent of every automated action within those ecosystems.