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More than ten years ago, the Dragonfly (aka “Energetic Bear”) espionage campaign targeted smaller pharma suppliers to steal intellectual property. By 2017, the NotPetya outbreak crippled Merck & Co., causing an estimated $1.3 billion financial blow to the firm, while forcing it to borrow vaccine stockpiles from the U.S. government. In March 2023, India’s largest drugmaker, Sun Pharmaceutical Industries, became another cautionary tale after the BlackCat/ALPHV ransomware group stole company data and disrupted operations. These high-profile attacks underscore one point: pharmaceutical companies remain prime targets for cybercriminals, with everything from vaccine stockpiles to intellectual property in the crosshairs.
Lowering the barrier to entry for cybercriminals
Not too long ago, a would-be hacker had to be at least a “script kiddie”—prowling underground forums for exploit code or assembling creaky malware toolkits—to pose a real threat. But thanks to artificial intelligence, almost anyone with enough determination and malicious intent can launch a damaging attack.
“Hackers don’t need to be that advanced anymore,” says Siwar El-Assad, head of cybersecurity at QuantHealth, pointing to how AI tools lower the bar for new attackers. She notes that hackers can easily rent ransomware online or leverage AI to craft realistic phishing emails—no deep networking expertise required. And in the longer run, quantum computing threatens to render many of today’s common encryption techniques obsolete.
Before, you had to really understand networks and vulnerabilities, but nowadays you can rent ransomware or ask AI to write a very realistic phishing email—and you just send it. That basically makes life easier for hackers.
And for elite hackers, AI stretches even further. While guardrailed chatbots like ChatGPT limit certain malicious uses, specialized generative AI models such as WormGPT have surfaced on underground forums, helping advanced threat groups automate zero‑day exploitation, craft undetectable malware, and generate deepfake‑driven social engineering at scale. Official advisories confirm that state‑sponsored actors such as China‑backed Volt Typhoon have exploited vulnerabilities in FortiGate devices using similar tactics—while validated Common Vulnerabilities and Exposures (CVEs) like CVE‑2024‑5565 in Vanna.AI, CVE‑2024‑6983 in LocalAI, CVE‑2024‑8396 in Deep Java Library, and CVE‑2024‑7474 in Lunary demonstrate how prompt injection, malicious configuration, and IDOR flaws are being weaponized with AI. They also enable more convincing phishing schemes, combining advanced targeting with deepfake video or audio so realistic it can fool even vigilant employees into handing over credentials.
A timeline: major pharma cyberattacks also hold lessons for other R&D-intensive industries

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2014: Dragonfly espionage via the supply chain
- Target and tactics: A state-backed Russian group known as Dragonfly infiltrated small third-party suppliers to reach major pharmaceutical manufacturers.
- R&D disruption angle: By trojanizing software updates for industrial control systems, attackers gained access to proprietary drug formulas and batch processes—critical intel for ongoing R&D.
- Key lesson: Supply-chain breaches remain one of the top threats to R&D teams, often exploiting the weaker security of smaller vendors.
2017: NotPetya cripples Merck’s network
- Nature of attack: A destructive wiper malware masquerading as ransomware (NotPetya) tore through Merck & Co.’s systems.
- R&D fallout: The attack forced Merck’s labs and manufacturing plants offline for weeks—interrupting vaccine production and stalling research projects. Employees lost access to critical data, hindering both current studies and future innovations.
- Business impact: Losses exceeded $1 billion, illustrating the massive financial damage that a prolonged R&D and production shutdown can inflict.
2018–2019: Winnti’s espionage strikes Bayer & Roche
- Methodology: The Chinese-linked Winnti group used stealthy malware with stolen digital certificates to penetrate networks at Bayer (2018) and Roche (2019).
- R&D threat: Though operational disruption was minimal, the real risk was unauthorized access to clinical trial results, new drug candidates, and other valuable IP. Early detection limited the damage—but underscored the vulnerability of leading research hubs.
- Notes: Espionage-centric intrusions can remain undetected for months, gathering data that may benefit competitors or illicit drug manufacturers.
2020: Ransomware hits Dr. Reddy’s mid–COVID-19 trials
- Attack details: Just as Dr. Reddy’s Laboratories began trials for Russia’s Sputnik V vaccine, ransomware forced a shutdown of plants and data centers worldwide.
- Research disruption: Trial data was at risk, and production lines ground to a halt. Although downtime lasted about a day, any delay in pandemic-related R&D was especially high-stakes.
- Broader context: Threat actors aggressively targeted vaccine developers in 2020, eyeing confidential data with both financial and political motivations.
2020: Attacks on Pfizer/BioNTech & AstraZeneca
- Pfizer/BioNTech – EMA breach: Hackers accessed regulatory review documents for the COVID-19 vaccine and leaked altered files online, potentially undermining public trust.
- AstraZeneca – phishing attempts: North Korean operatives spear-phished scientists to infiltrate vaccine research systems. Though ultimately unsuccessful, the campaign highlighted the relentless focus on high-value R&D data.
- Key takeaway: Even when core servers remain unbreached, hackers can manipulate or leak partial data—jeopardizing public perception and regulatory confidence.
2023: Sun Pharma ransomware (BlackCat)
- What happened: India’s largest drugmaker, Sun Pharma, fell victim to the BlackCat/ALPHV ransomware gang, resulting in significant data theft and system downtime.
- R&D and operational effects: Sun Pharma had to isolate networks—temporarily disrupting certain manufacturing processes and R&D projects. The company warned of a revenue drop due to compromised operations and potential legal repercussions.
- Implication: Even the biggest pharma players with global footprints are vulnerable to ransomware that can derail scientific progress and hamper revenue streams.
A growing attack surface
Meanwhile, pharmaceutical companies are using AI to accelerate drug discovery, inadvertently creating more complex data pipelines—and thereby more opportunities for attackers. “When you have AI, your data might be processed or stored multiple times,” El-Assad says, summarizing the challenge from a security perspective. “You have it going through ingestion, analysis, model training—each stage is an opportunity if it’s not secured end-to-end. If there’s a man-in-the-middle attack in that pipeline, they can read your data unless you’re fully encrypting it and monitoring for anomalies.”
The stakes are high. From patient safety to regulatory compliance to billions of dollars’ worth of intellectual property, pharma’s appeal to cybercriminals is clear. El-Assad warns that this “digitization of patient information, clinical trial details, or proprietary research” exposes many potential entry points for attackers, adding that a best practice is “to assume breach and catch threats early.”
Guarding sensitive IP and patient data
At QuantHealth, machine-learning analytics drive a proactive threat-hunting program that scans for anomalies that might signal an intrusion at an early stage. “We use AI and machine learning to learn what’s normal for each user and device,” El-Assad says. “If someone manages to get in—maybe through phishing or a vulnerable server—we want to spot any suspicious behavior right away, before they can escalate privileges or exfiltrate data.”
This vigilant approach also extends to monitoring the flow of data through each stage of the AI pipeline so that a man-in-the-middle attack won’t reveal critical clinical trial data midstream. El-Assad emphasizes the consequences of letting external AI services learn from proprietary data: “You’re basically exposing your data to the internet, and that data effectively becomes public,” she says, stressing that employees must be trained to avoid pasting or uploading sensitive materials into personal AI tools.
At QuantHealth, an “assume breach” mindset applies across every layer of the company—from R&D teams to external collaborations. Rather than rolling out AI tools and integrations without proper checks, the company relies on zero‐trust network segmentation, continuous anomaly detection, and strict role‐based access controls to contain intrusions. Designing the environment with the expectation that attackers can eventually break in helps QuantHealth limit damage quickly—safeguarding patient data, preserving clinical trial integrity, and enabling pharmaceutical innovation to continue securely.
An evolving cyber game of cat and mouse
While AI grants new powers to cybercriminals, it also gives defenders an edge. As El-Assad explains, “The right strategy is to be proactive. We’re using AI for defensive security—so once there’s a slight deviation in user or device behavior, we’re alerted and can contain the threat.”
In addition to external dangers, insider threats loom large—especially among staff who might be tricked by phishing emails or social-engineering scams. Attackers can also compromise data integrity by tampering with clinical trial inputs, potentially undermining patient safety and research outcomes. Supply-chain attacks pose another major risk if vendors fail to patch vulnerabilities, giving attackers a direct path into drug-development pipelines. Moreover, while encryption remains essential, the emerging threat of quantum computing—which could one day break present-day cryptographic standards—forces pharma to plan ahead for next-generation data protection.
To protect against attackers, put yourself in the shoes of one
“Having done ethical hacking, I understand the process from the attacker’s side,” says El-Assad. “You learn to predict their next steps—privilege escalation or command-and-control—and shut it down before they exfiltrate data. That might mean luring them into honeypots or isolating devices in the network so they can’t pivot further. It’s a constant cat-and-mouse game, but knowing how the adversary thinks helps us stay one step ahead.”
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