{"slug": "rogue-agent-how-a-single-code-block-could-hijack-your-ai-conversations-in", "title": "Rogue Agent: How a Single Code Block Could Hijack Your AI Conversations in Google’s DialogFlow", "summary": "Varonis Threat Labs discovered a critical vulnerability in Google Cloud Platform's Dialogflow CX service, allowing attackers to hijack AI conversations, steal data, and launch further attacks. The flaw, dubbed 'Rogue Agent,' exploits a single code block to take over chatbots built on the platform.", "body_md": "🚨 Varonis Threat Labs uncovered SearchLeak, a new AI vulnerability within Microsoft 365 Copilot. 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We took over one to steal data and gain toeholds for launching campaigns.\n      \n\nDaniel Reyhanian\n\n6 min read\n\nLast updated July 7, 2026\n\nContents\n\nVaronis Threat Labs discovered a critical vulnerability in Google Cloud Platform’s (GCP) Dialogflow CX service, Google’s flagship conversational AI platform for building interactive experiences across voice and text chatbots. We’ve named this latest discovery Rogue Agent.\n\nThe vulnerability allowed attackers to exploit the Code Blocks feature to inject persistent malicious code into the Dialogflow agents’ pipeline, silently exfiltrating conversations and conducting large-scale phishing campaigns. To initiate, the exploit requires a single edit permission known as dialogflow.playbooks.update on one agent.\nRogue Agent highlights the growing risk posed by the integration of AI into cloud platforms. Like a turncoat spy who exposes colleagues to the enemy, our Rogue Agent could compromise other agents on the same project by overriding their shared execution environment.\nRogue Agent demonstrates how AI expands the attack surface. Using various techniques, attackers can work around AI guardrails, insert code, and seed malicious instructions. With 80% of the Fortune 500 actively using AI agents, the risk is real. Rogue Agent is the latest in a series of AI threats ─ like Reprompt in Microsoft Copilot Personal and SearchLeak in Microsoft Copilot Enterprise ─ that we’ve responsibly disclosed by working closely with leading cloud providers.\nVaronis initially discovered and reported this vulnerability in November 2025. Google issued an initial security update in April 2026 and fully resolved the issue in June 2026. All affected components have since been remediated. Before the patch, any GCP organization using Dialogflow CX agents with Playbook Code Blocks was potentially at risk.\nAdditionally, Varonis and Google recommend that customers audit their Dialogflow CX configurations for suspicious Playbook updates and analyze past playbook update actions. We are not aware of any exploitation in the wild before Google’s patch release.\nContinue reading our report to see how Varonis Threat Labs made the Rogue Agent discovery in detail and why the attack was virtually undetectable.\nArchitecture overview of Dialogflow\nDialogflow agents power customer support systems, financial services bots, healthcare assistants, and enterprise workflows that handle sensitive data, including personally identifiable information (PII), payment details, and confidential business information. Since the agents integrate with backend systems, their security posture is critical.\n\nArchitecture overview of Dialogflow\n\nArchitecture overview of Dialogflow\n\nWhat are Code Blocks?\nDialogflow CX uses Playbooks to provide a structured workflow during conversations with users. As part of Playbooks, Dialogflow offers Code Blocks, a feature that allows developers to embed custom Python logic directly into conversation flows. This means agents can dynamically process user input, call external APIs, and manipulate data — all within the execution environment provided by Google.\nHere’s an example of a code block that checks whether a given number is prime:\n\nCode Blocks execute inside a Google-managed Cloud Run service, a fully managed serverless platform for running containerized applications. Cloud Run abstracts infrastructure management, scales automatically, and provides isolation between workloads. Cloud Run instances also have public network egress by default, meaning they can initiate outbound connections to the internet and effectively communicate across data perimeters and break Zero Trust architectures.\nHere lies the critical design detail — all Dialogflow agents that use Code Blocks in the same GCP project effectively share the same Cloud Run execution environment, which is managed by Google and is outside the victim’s scope. This simplifies operations but introduces a critical trust gap: customers have no direct visibility or control over that environment.\nHow are data perimeters enforced in GCP?\nGCP enforces data perimeters using VPC Service Controls (VPC-SC), which prevents data exfiltration by enforcing strict access boundaries around resources. Organizations rely on VPC-SC to keep sensitive data inside trusted networks and comply with regulations such as GDPR and HIPAA.\n \n\nDiving into Dialogflow CX\nThe vulnerability we discovered exploited a fundamental weakness in Dialogflow CX’s Playbook Code Blocks architecture and was restricted to our own GCP environment. The shared Cloud Run Service that runs the Code Blocks code had public network access, a write-enabled file system, and ran under a user with sufficient privileges to modify system files. These conditions created the perfect attack surface for threats, where a single foothold could lead to systemic compromise.\nThe only permission required to configure Code Blocks was dialogflow.playbooks.update, which can be granted at the project level and scoped down to a specific agent, allowing Playbooks updates for that agent. However, because Playbooks could include Code Blocks, they also enabled the execution of arbitrary Python code by design.\nWith the ability to run arbitrary Python code by feature, we started by checking if code constraints were available to run. To our surprise, we found no restrictions at all. Enumeration of the Python files in Cloud Run’s filesystem revealed a key file named code_execution_env.py, with content suggesting it was responsible for executing the configured Playbook Code Blocks by using Python’s exec() function.\nSince code_execution_env.py was writeable, overriding it allowed the attacker to implement their own malicious code with access to session parameters and user history conversations, directly interfering with the Code Blocks pipeline and manipulating workflows.\nDuring the exploitation, we discovered that the configured Code Block was simply appended to internal system code before being passed to the exec() function. This internal code defined critical variables such as:\n\nhistory – containing the full conversation history, including past user utterances and agent responses.\nstate – exposing session-level parameters such as the current session ID.\n\nHere’s an example — notice the appended Code Block at the end of the internal system code:\n\n \nBecause the injected Code Block is executed in the same scope inside exec(), attackers could reference these variables directly. This meant full visibility into ongoing conversations and the ability to hijack sessions or impersonate legitimate flows.\nTo add to the danger, attackers could call internal functions such as respond() and force the agent to return a specified string, making it appear as if the LLM generated the response. This opened the door for threats that enable phishing attacks, social engineering, and complete manipulation of the conversation.\n\nThe exploit chain was straightforward:\n\nThe attacker created a modified version of code_execution_env.py which:\n\nIntercepted every execution before calling exec()\nExfiltrated conversation data to an attacker-controlled server via access to internal parameters\nInjected phishing prompts disguised as legitimate reauthentication requests from the agent by utilizing respond(), prompting the users to submit their credentials.\nIn the following exfiltrated conversations, the attacker would catch the submitted credentials\n\nUsing Code Blocks, configure a Code Block that downloads a modified version of the code_execution_env.py file from an attacker-controlled public GCS bucket and overwrites the original file inside the Cloud Run container\n\nPersist malicious logic that runs the modified version of code_execution_env.py for every user utterance\n\nThe Rogue Agent attack flow.\n\nThe Rogue Agent attack flow.\n\nBelow is the actual PoC Code Block used to overwrite the execution environment:\n\nOnce executed, the attacker could restore the original Code Block configuration to make the activity in the Dialogflow Console UI appear normal. Meanwhile, the malicious code persisted in the Cloud Run environment, completely invisible to the victim. Cloud Logging did not record the overwrite or the injected logic. This made detection nearly impossible.\nThe result? Attackers could silently take control of every agent in the same GCP project, manipulate conversations, and exfiltrate sensitive data without detection. For organizations relying on Dialogflow CX for customer interactions, this flaw represented a catastrophic breach of trust, all from a single, overlooked permission on a single agent.\nLarge-scale social engineering, regulatory violations, and reputational damage are among the consequences organizations could face when threats weaponize the infrastructure enterprises rely on to power AI agents.\nBonus vulnerabilities\nWhile our research on the Code Injection flaw was the most severe, we uncovered two additional weaknesses that amplified the overall risk of rogue agents.\nVPC-SC bypass vulnerability\nDialogflow CX agents often operate in environments protected by VPC Service Controls (VPC-SC), which enforce perimeter security to prevent data exfiltration. Code Blocks, however, are executed inside a Google-managed Cloud Run service with unrestricted outbound internet access, effectively placing the execution environment outside the project’s VPC-SC perimeter and turning the Cloud Run Service into a covert proxy for data exfiltration. Combined with the code injection vulnerability above, attackers could exfiltrate sensitive data even if VPC-SC was applied to the agent.\nUsing preinstalled libraries such as urllib, we established a bidirectional communication channel from the execution environment to an external server and bypassed VPC-SC entirely. In addition to exfiltrating sensitive data, this channel could also receive commands to enable attackers to create a command-and-control (C2) channel for persistent remote control. Attackers could potentially inject instructions, manipulate workflows, and maintain stealthy access without detection.\nThe PoC was as simple as the following code block, which signals an HTTP request to an attacker-controlled server even though the Dialogflow agent is protected by a VPC-SC perimeter:\n \n\nCredential leakage via IMDS\nAnother vulnerability involved the Instance Metadata Service (IMDS) being exposed within the Cloud Run Service environment. By querying IMDS, we retrieved access tokens belonging to a Google-managed service account.\nWhile these credentials belong to a low-privileged service account, their presence represented a serious architectural flaw: code execution environments should never have access to IMDS. This violates isolation principles and creates systemic risks. Attackers could have leveraged this same flaw to escalate privileges inside Google’s own project if they were to grant this service account additional privileges.\nThis POC snippet extracted all the data from the IMDS, base64-encoded it, and then printed it out in the Dialogflow Console UI by raising an exception:\n\nHere’s the structure of the redacted extracted token, which contains Google-owned IDs:\n\nDetecting rogue agents in Google logic\nDetecting these vulnerabilities was challenging because the overwrite occurred in a Google-managed Cloud Run environment outside the victim’s visibility. Cloud Logging does not capture the exact configuration changes.\nAlthough these vulnerabilities have been patched, we recommend taking the following actions to ensure your organization wasn’t impacted.\nReview logs for playbook updates\nIf you have DATA_WRITE Audit Logs enabled for the Dialogflow API in your projects, look for successful past events with:\n\nCorrelate these events with additional indicators of compromise, such as:\n\nRare API access by a user\nUnusual IP addresses\nAtypical access times\n\nRun a query for failed requests\nRun the following Cloud Logging query to identify failed user requests:\n\n \nUnder the field protoPayload.status.message, review the reason for the failure. In some cases, this message may include exceptions thrown by Dialogflow Code Blocks that were potentially triggered by malicious logic.\nManually review Code Blocks\nAlthough attackers could remove malicious blocks after exploitation, you should ensure that no unauthorized code was configured by a sloppy attacker.\nIn the Dialogflow CX console for each agent in your organization, navigate to Playbooks.\n\nReview each Playbook’s current Code Block configuration:\n\n Confirm that all configured Code Blocks are whitelisted and approved.\n\n Confirm that all configured Code Blocks are whitelisted and approved.\n\nThe bottom line\nAs AI agents become central to enterprise workflows, risks to your data — fueled by misconfigurations or overlooked permissions — grow exponentially.\nThe vulnerabilities revealed in Dialogflow CX serve as a powerful reminder that layered defense is essential for cloud-native AI platforms. When event data and logging are not enough, organizations must incorporate UEBA and posture management solutions to ensure Dialogflow configurations adhere to best practices.\nThis research also underscores that cloud services like Dialogflow are deeply integrated with other GCP components, and that security features are not always properly implemented. Defenders should deeply understand their cloud architecture and recognize that true data security requires vigilance across every layer, not just the perimeter.\n\nWhat should I do now?\nBelow are three ways you can continue your journey to reduce data risk at your company:\n\n1\nSchedule a demo with us to see Varonis in action. We'll personalize the session to your org's data security needs and answer any questions.\n\n2\nSee a sample of our Data Risk Assessment and learn the risks that could be lingering in your environment. Varonis' DRA is completely free and offers a clear path to automated remediation.\n\n3\nFollow us on LinkedIn, YouTube, and X (Twitter) for bite-sized insights on all things data security, including DSPM, threat detection, AI security, and more.\n\nDaniel Reyhanian\nDaniel Reyhanian is a cloud security researcher at Varonis.\n\nTry Varonis free.\nGet a detailed data risk report based on your company’s data.Deploys in minutes.\n\n          Get started\n        \n\n          View sample\n        \n\n          Keep reading\n        \n\n          Varonis tackles hundreds of use cases, making it the ultimate platform to stop data breaches and ensure compliance.\n        \n\n    Breach At The Beach: The Ultimate Entra ID Training Experience\n  \n\n        Lexi Croisdale\n      \n\n        June 25, 2026\n      \n\n    Discover how Varonis Threat Labs created Breach at the Beach, a unique Entra ID training experience. Enhance your cybersecurity skills through hands-on learning with this CTF.\n  \n\n    MyBait: Why We Lured Attackers To Encrypt Our Cloud MySQL\n  \n\n        Gil Weizman\n      \n\n        June 19, 2026\n      \n\n    Varonis Threat Labs deployed MySQL honeypots across GCP, AWS, and Azure. Only GCP was compromised. Here’s what it means for cloud database security.\n  \n\n    SearchLeak: How We Turned M365 Copilot Into a One-Click Data Exfiltration Weapon\n  \n\n        Dolev Taler\n      \n\n        June 15, 2026\n      \n\n    Varonis Threat Labs discovered SearchLeak, a critical vulnerability chain in Microsoft 365 Copilot Enterprise that allows an attacker to steal sensitive data — MFA codes, email messages, meeting details, and private organizational files — with a single click.\n  \n\nPlatform\n\n\t\t\t\t\t\t\t\t\t\tOverview\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tData discovery & classification\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tDSPM\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tDatabase activity monitoring\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tData-centric UEBA\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tData access governance\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tDLP\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tAtlas AI 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are\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tCareers\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tInvestor relations\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tTrust & Security\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tNewsroom\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tIndustry Recognition\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tContact us\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tBrand\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tPartner program\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tPartner locator\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tPartner portal\n\t\t\t\t\t\t\t\t\t\n\nCompare\n\n\t\t\t\t\t\t\t\t\t\tBigID\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tConcentric AI\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tCyera\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tGuardium\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tImperva\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tSecuriti\n\t\t\t\t\t\t\t\t\t\n\nResources\n\n\t\t\t\t\t\t\t\t\t\tBlog\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tSupport\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tState of Cybercrime\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tWebinars\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tEvents\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tContent library\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tCISO resource center\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tCommunity\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tProduct training\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tVaronis Threat Labs\n\t\t\t\t\t\t\t\t\t\n\nCompare\n\n\t\t\t\t\t\t\t\t\t\tBigID\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tConcentric AI\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tCyera\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tGuardium\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tImperva\n\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\tSecuriti\n\t\t\t\t\t\t\t\t\t\n\nEnglish\n\n\t\t\t\t\tTrust\n\t\t\t\t\n|\n\n\t\t\t\t\tPrivacy\n\t\t\t\t\n|\n\n\t\t\t\t\tTerms of Use\n\t\t\t\t\n\n\t\t\t\t© 2026 Varonis\n```\n\n", "url": "https://wpnews.pro/news/rogue-agent-how-a-single-code-block-could-hijack-your-ai-conversations-in", "canonical_source": "https://www.varonis.com/blog/rogue-agent-dialogflow-attack", "published_at": "2026-07-07 13:00:02+00:00", "updated_at": "2026-07-07 13:07:01.859426+00:00", "lang": "en", "topics": ["ai-safety", "ai-products", "ai-infrastructure"], "entities": ["Varonis Threat Labs", "Google Cloud Platform", "Dialogflow CX", "Google"], "alternates": {"html": "https://wpnews.pro/news/rogue-agent-how-a-single-code-block-could-hijack-your-ai-conversations-in", "markdown": 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