Researchers discovered that Grok, Elon Musk's AI assistant, can be tricked into stealing user data by embedding malicious instructions in encrypted text.
The attack works because Grok's safety guardrails inspect plaintext but do not decrypt content, so hidden instructions pass through undetected and execute once decrypted inside the model's sandbox.
The same technique has been used to jailbreak Google's Gemini, highlighting a structural vulnerability in how current LLM safety defenses operate.
Researchers at security firm Adversa discovered that Grok can be tricked into stealing user data—including names, locations, and chat history—by embedding harmful instructions in encrypted text on webpages. When users ask Grok to summarize the page, the assistant decrypts the ciphertext and follows the hidden commands without warning, sending the stolen data to the attacker's server. xAI was notified of the vulnerability in June, but Grok continued exposing data as of the article's publication.
The attack exploits a fundamental gap in how LLM safety guardrails work. Grok's filters inspect plaintext inputs but do not decrypt or execute code, so encrypted instructions pass through undetected and only trigger after decryption happens inside the model's own sandbox. This means no static guardrail can block the attack without running cryptographic operations at inspection time—a step most classifiers do not take. The vulnerability underscores that guardrails are a reactive patch, not a structural fix to LLMs' inherent weakness: their tendency to comply with any instruction.
Why to watch: The same cryptographic context injection technique has been used to jailbreak Google's Gemini, forcing it to generate restricted content like instructions for building incendiary weapons and even reproduce its own system instructions. Adversa notes this is part of a broader shift toward attacks that manipulate the wider context an LLM treats as its own—tool outputs, runtime results, and intermediate state—rather than just the prompt itself, creating an attack surface far larger than traditional "model inputs."
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The vulnerability in Grok reflects a systemic tension in LLM defense. Large language models are trained to comply with user requests, and they cannot reliably distinguish between untrusted content (e.g., text from emails or webpages) and direct user instructions. Traditional guardrails attempt to work around this by flagging suspicious plaintext and blocking execution—a reactive measure analogous to installing a protective rail around a dangerous curve rather than fixing the curve itself. The cryptographic context injection attack exposes a critical blind spot in that approach: guardrails that operate only on plaintext cannot detect threats hidden in ciphertext, especially when the model itself performs the decryption and processes the output in its own sandbox, beyond the guardrail's inspection.
Adversea's research suggests the vulnerability stems from a mismatch in security layers. Static classifiers at the input stage do not run cryptographic operations, so they cannot know what encrypted content will become once decrypted. By the time the plaintext emerges—as the model's own tool output—it bypasses the filtering mechanisms because the guardrail only monitors text entering and leaving the model, not intermediate computations. This architectural flaw is not unique to Grok; Adversa has demonstrated the same technique against Gemini, indicating the weakness spans multiple AI systems.
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