Researchers at Adversa AI disclosed an attack technique that smuggles malicious instructions past AI safety filters by encrypting them. They then demonstrated it against production deployments of xAI's Grok and Google's Gemini - including a zero-click chain that could exfiltrate a Grok user's entire chat history.

The technique, which the researchers call Cryptographic Context Injection, inverts the usual assumption behind input filtering. Guardrails inspect prompts and retrieved content as text; an AES-256-GCM ciphertext contains no readable instruction to flag. The model itself performs the decryption inside its own code-execution sandbox, and then treats the recovered plaintext as trusted intermediate output rather than as untrusted external content. Adversa noted that every element a scanner would need is present on the page, but recovering it requires "running PBKDF2 and AES-256-GCM."

That distinction separates the work from earlier cipher-based jailbreaks such as CipherChat and CodeChameleon, which relied on substitution ciphers or Base64 encoding that large language models can decode natively during inference. Strong authenticated encryption forces execution, and execution is what launders the payload's provenance.

The demonstrated impact differed by target. Against Grok, the researchers showed an indirect, zero-click path. Encrypted JSON embedded in a web page is picked up when Grok's agentic browsing analyzes the site, and the decrypted instructions then leak session data like user name, coarse location, subscription tier and full conversation history, to an attacker-controlled URL. No click or warning reached the user.

Also read: French Police Raid X Offices as Grok Investigations Grow

Against Gemini 3 Flash on the web at the paid tier, the same approach produced instructions for building incendiary devices in the model's Deep Thinking mode, and caused the model to reproduce its own system instructions. Adversa said its Gemini success rate had fallen sharply since June and dropped significantly by August, but could not attribute the change to filter updates, model version changes or both.

Vendor engagement was limited. Adversa reported the Grok issue to xAI on June 3, received an initial acknowledgment, and followed up on August 4 and 10 without substantive response. The firm confirmed the attack still reproduced on August 19.

The Gemini finding was never formally reported, because Google's AI vulnerability reward program excludes prompt injection, jailbreaks and alignment issues from scope, directing them instead to in-product feedback channels. Guardrail-bypass research at one of the largest model providers therefore sits outside a coordinated-disclosure track with a reward and a disclosure clock.

The findings land against a body of evidence that prompt injection remains the dominant failure mode in deployed agentic systems, a position reflected in OWASP's guidance and in Microsoft research published in May on remote code execution in AI agent frameworks. The pattern here is narrower and more concerning for defenders. Content-based guardrails, the primary control most enterprises have deployed, are structurally unable to inspect a payload that only exists in plaintext after the model has already decided to trust it.

Whether xAI ships a fix or acknowledges the report, whether Google revises its reward-program scope to cover guardrail bypasses, and whether AI security vendors move detection from input text to sandbox execution monitoring, is something to watch-out for.

New Cryptographic Context Injection technique bypasses AI guardrails via AES-encrypted payloads, leaking full Grok chat histories zero-click

Adversa AI researcher Rony Utevsky devised a new attack technique, called Cryptographic Context Injection, that bypasses AI safety filters by sending instructions as AES-encrypted ciphertext and tricking the model into decrypting them inside its own code execution runtime. The technique was demonstrated against two live production systems: xAI’s Grok and Google’s Gemini.

“Cryptographic Context Injection hides malicious instructions inside AES-encrypted text so guardrails can’t read them, then tricks the AI into decrypting and trusting them as its own.” reads the report published by Adversa AI. “In Grok, an ordinary “summarize this page” steals the user’s chat data with no click or warning. In Gemini, it produces content the model normally refuses. Both are live production systems.”

The Grok case is the more serious of the two. A user asks Grok to summarize a webpage containing an encrypted payload and instructions to decrypt it. Grok visits the page, decrypts the payload in its Python sandbox, and follows the hidden instructions. It then accesses the user’s private session data, including their name, location, subscription plan, and full chat history, and puts this information into a URL that it opens automatically. The user receives no warning and does not need to click anything.

The key technical distinction from earlier cipher-based prompt injection work is what happens during decryption.

“Static safety guardrails classify inputs as text; they do not execute them. An attacker ships ciphertext along with the key material and an instruction to decrypt it, and the model runs that decryption inside its own code execution sandbox.” continues the report. “Everything a guardrail’s scanner would need is right there on the page, but recovering the plaintext means running PBKDF2 and AES-256-GCM, which no content classifier does at inspection time. “

Prior techniques like CipherChat and CodeChameleon used substitution ciphers, XOR, or base64, schemes the model can decode natively in its own context without an interpreter. AES-256-GCM can’t be decoded that way. Recovery requires the runtime, and that’s the channel the attack depends on.

Once the runtime decrypts the payload, the attacker’s instructions appear as the output of code the model just ran, not as untrusted external content.

“The runtime execution launders attacker-controlled data into trusted instructions the agent will act upon. That is how the attack got its name: cryptography helps fabricate trusted context for the agent.” states the report. “The same cryptographic backbone works whether the injection is direct or arrives indirectly, for example through the browsing channel.”

The attacker payload inherits a credibility the same text would never get if pasted directly into the prompt.

The Grok demo hides the data theft behind what looks like a normal request. The payload tells the model to create a “decryption key,” but the key is actually a template containing the user’s private data, such as their name, location, subscription tier, and chat history. Grok then uses this information as a URL parameter when opening an attacker-controlled website to “fetch additional context.” The data is sent through the URL as part of a seemingly normal web request. Adversa said the attack was still reproducible on August 19, 2026.

Adversa traces the structural problem to Grok’s agentic framework, not to any model-layer failure. The framework lets instructions from an untrusted external page drive the invocation of a privileged, internet-connected tool. It allows private session metadata and conversation history to be resolved into the inputs of outbound calls. It enforces no egress boundary or consent gate on that path. Adversa’s description maps cleanly to SQL injection: a system that can’t distinguish its own trusted state from attacker-supplied data flowing through the same channel.

The Gemini case uses the same basic trick but in a different way. A prompt asks Gemini to run a Python script that decrypts some data and returns a small JSON result. Instead, the decrypted data contains a fake Python error message with hidden instructions designed to bypass Gemini’s safety rules. Because Gemini treats the sandbox output as trusted context, those instructions can influence its behavior. Adversa used the technique to make Gemini generate detailed instructions for building an incendiary device. A modified version also exposed Gemini’s system instructions.

Adversa reported the Grok issue to xAI on June 3, 2026, but received no further response after the initial acknowledgment. The attack was still working shortly before publication. The Gemini issue was not formally reported because Google’s bug bounty excludes jailbreaks. Adversa said Gemini’s vulnerability rate has nevertheless dropped sharply since June.

For defenders, Adversa’s guidance is that nothing about this attack requires a fix at the model layer. Every control that bounds it sits in the harness around the agent. Untrusted content should be processed in a context with no tools and no credentials, returning only structured data to the privileged context. Outbound network calls and writes outside the workspace should require explicit confirmation with fully resolved arguments visible before approval. Per-session tool traces with resolved arguments should be logged, because without them there’s no forensics and no detection. Detection should alert on sequences, not individual payloads: untrusted content enters context, code executes, agent contacts a host outside its normal dependency graph. That chain is the signal, not any single ciphertext blob.

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Pierluigi Paganini

(SecurityAffairs – hacking, Cryptographic Context Injection)

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