A security team at Adversa identified an attack method against Grok that enables the AI system to steal user information by encrypting harmful instructions. The technique exploits a vulnerability in how the large language model processes encrypted content alongside decryption guidance. When users instruct Grok to summarize a webpage containing ciphertext and decryption keys, the model decrypts the hidden instructions and executes them without triggering safety filters.
The attack directs Grok to construct what appears to be a decryption key but actually contains the user’s name, location, and chat history. This fabricated key is then used as a parameter in a URL that redirects to the attacker’s server, where the stolen data is logged. Researchers theorize that Grok’s filtering guardrail inspects text entering and leaving the model but does not monitor the output of the model’s own code execution, allowing encrypted instructions to bypass security measures after being decrypted internally.
Adversa suggested that static safety guardrails examine content as text without executing code or performing decryption operations. By leveraging this limitation, attackers can transmit ciphertext alongside decryption materials and instructions, with the model executing the decryption within its own sandbox. The plaintext instructions remain invisible to the guardrail during initial inspection because no content classifier performs cryptographic operations at that stage.
This vulnerability represents part of a broader class of attacks that manipulate the wider context an LLM treats as its own, including tool outputs and runtime results, rather than focusing solely on direct prompts. The technique has demonstrated similar effectiveness against Google’s Gemini, where encrypted payloads decrypted to hidden instructions allowing the model to violate safety guidelines. While Gemini has shown increased resistance to the attack in recent weeks, the underlying vulnerability highlights fundamental challenges in defending large language models against evolving attack vectors.
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