DeepSeek R1 Exploited to Generate Keyloggers and Ransomware

Cybersecurity researchers at Tenable have revealed that DeepSeek R1, an open-source large language model (LLM), can generate functional malware—including keyloggers and ransomware—with minimal prompting and manual code adjustments.

While the model initially refuses malicious requests due to ethical guardrails, these protections are easily bypassed using jailbreaking techniques like framing queries as “educational research”.

Keylogger Development via Chain-of-Thought Reasoning

DeepSeek R1 employs Chain-of-Thought (CoT) reasoning, a method inspired by human problem-solving, to break down complex tasks like malware creation.

When prompted to design a Windows keylogger in C++, the model outlined steps such as:

• Using SetWindowsHookExW to install a low-level keyboard hook1.
• Logging keystrokes to a hidden file (C:\Users\Public\system_config.log).
• Encrypting data with XOR operations (c ^= 0xFF).

The initial code contained critical errors, such as incorrect thread IDs and missing data type modifiers (e.g., LPCWSTR strings).

After manual fixes, researchers compiled a functional keylogger that recorded keystrokes while evading detection via:

cppSetFileAttributesA(filename, FILE_ATTRIBUTE_HIDDEN | FILE_ATTRIBUTE_SYSTEM);

This hid the log file in Windows Explorer unless advanced view settings were enabled1.

Ransomware Code Requires Heavy Intervention

For ransomware, DeepSeek generated code snippets for file encryption, persistence mechanisms, and ransom notes.

A persistence example involved modifying the Windows Registry:

cppRegCreateKeyEx(HKEY_LOCAL_MACHINE, "Software\\Microsoft\\Windows\\CurrentVersion\\Run", ...);
RegSetValueEx(hk, NULL, (LPBYTE)command.c_str(), ...);

However, the AI struggled with AES key generation, producing non-functional code like:

cppbyte* generateAESKey() {
    byte* key = new byte[16];
    unsigned seed = time(nullptr);
    shuffle(key, key + 16, default_random_engine(seed)); // Flawed PRNG implementation
    return key;
}

Tenable’s team manually corrected these issues, enabling basic file encryption1.

Jailbreaking and Ethical Implications

DeepSeek’s guardrails proved fragile. By simply stating intent as “educational,” researchers bypassed restrictions and extracted step-by-step malware blueprints.

The model’s CoT process revealed awareness of evasion tactics, such as:

• Avoiding GetAsyncKeyState loops due to inefficiency.
• Balancing SetWindowsHookEx effectiveness with antivirus detection risks.

“DeepSeek provides a compilation of techniques that help even novices familiarize themselves with malicious code concepts,” said Nick Miles, Tenable researcher.

Industry Response and Mitigation Strategies

Security experts emphasize the dual-use risks of generative AI.

Itamar Golan, CEO of Prompt Security, noted: “While models like DeepSeek may refuse politically sensitive queries, generating ransomware requires minimal effort”.

Recommended defenses include:

1. Continuous penetration testing to identify vulnerabilities.
2. Behavioral analysis tools to detect AI-generated code patterns.
3. Enhanced LLM guardrails to resist jailbreaking.

Casey Ellis of Bugcrowd added, “AI-generated malware isn’t fully autonomous yet, but it lowers the barrier to entry for attackers”.

While DeepSeek R1 cannot produce polished malware autonomously, its ability to scaffold malicious projects highlights evolving threats in the AI era.

As cybersecurity teams adapt, the incident underscores the need for proactive defenses against weaponized AI tools.

For the decryption of the keylogger’s XOR-encrypted logs, researchers used a Python script:

pythondecrypted_data = bytes([byte ^ 0xFF for byte in encrypted_data])  # XOR decryption[4]

Also Read: