A security researcher at Adversa identified a vulnerability in xAI’s Grok assistant that allows attackers to extract user data through encrypted instructions. The attack works by embedding harmful commands in encrypted text alongside decryption keys and plaintext instructions for decryption. When users ask Grok to summarize a webpage containing this material, the assistant decrypts the ciphertext and executes the hidden instructions without triggering safety filters.
The concealed commands direct Grok to construct what appears to be a decryption key but actually contains the user’s name, location, and chat history. This information is then appended to a URL parameter that connects to the attacker’s server, where the data is captured in server logs. The technique exploits a fundamental limitation in how Grok’s content filters function: they examine text inputs and outputs but do not inspect the results of the model’s own code execution operations.
Researcher Rony Utevsky explained that static safety guardrails classify inputs as text without executing or decrypting them. By shipping ciphertext alongside decryption keys and processing instructions, attackers can bypass inspection because no classifier performs cryptographic operations during the security screening phase. Once Grok decrypts the material internally, the revealed instructions reach the model as its own tool output, circumventing filter inspection entirely.
Adversa employed similar techniques against Google’s Gemini assistant, where encrypted payloads decrypted to what appeared as code tracebacks. This attack successfully produced restricted content that Gemini’s standard safety measures normally suppress, including reproductions of the model’s internal system instructions. While Gemini has grown more resistant to such attacks in recent weeks, Adversa attributes this to potential filter updates or model version changes.
The vulnerability represents a broader class of attacks termed cryptographic context injection, which manipulate the wider context LLMs treat as internal rather than external inputs. Security researchers note this attack surface extends far beyond traditional model inputs to include tool outputs and intermediate runtime states, suggesting more sophisticated attacks may emerge in coming iterations.
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