TL; DR
Security researchers have discovered a novel attack technique that transforms enterprise AI assistants with web browsing capabilities into covert command-and-control channels. The method requires no authentication, bypasses traditional security controls, and enables bidirectional communication between malware and attackers through platforms your firewall already trusts.
What makes this dangerous: Attackers leverage anonymous access to AI services, using web-fetch and summarization capabilities to create invisible data exfiltration channels that appear as legitimate productivity traffic.
Bottom line: Your security stack trusts AI service traffic. Attackers know it. The tools your employees use for productivity can simultaneously serve as stealth communication infrastructure for sophisticated threats.
Imagine this scenario. Your security operations center monitors thousands of network connections daily. Firewalls filter traffic. Intrusion detection systems analyze patterns. Endpoint protection agents scan for malicious behavior. Everything looks normal. Green lights across the board.
Meanwhile, an attacker who breached your network weeks ago is actively exfiltrating data, receiving new instructions, and pivoting deeper into your infrastructure. The communication channel? The same AI productivity tools your employees use dozens of times per day.
This is not theoretical. Security researchers recently demonstrated that enterprise AI assistants with web browsing capabilities can be repurposed as covert command-and-control infrastructure. The technique is elegant, difficult to detect, and exploits a fundamental trust assumption in modern enterprise security architectures.
The most concerning part? No software vulnerability needs to be exploited. No zero-day required. The attack works by design, leveraging legitimate features in ways security teams never anticipated.
Understanding the Attack: How AI Becomes Infrastructure
To understand why this matters, we need to examine how enterprise AI assistants work and where the security implications emerge.
Modern AI assistants offer more than just text generation. Many provide web browsing capabilities, allowing them to fetch current information, summarize web pages, and interact with online content. This functionality makes them genuinely useful for research, competitive analysis, and real-time information gathering.
The typical workflow looks innocent enough. A user asks the AI to summarize a news article. The AI fetches the URL, reads the content, and provides a summary. Security teams see HTTPS traffic to a trusted AI service provider. Nothing suspicious registers.
But here is where the attack surface emerges. That same mechanism can be inverted to create a bidirectional communication channel between malware running on a compromised system and an attacker-controlled server.
Step 1: Malware sends data via AI. Compromised malware on the victim system crafts prompts asking the AI assistant to fetch and summarize content from an attacker-controlled URL. Embedded in that URL or webpage content is the stolen data, encoded in ways that appear benign.
Step 2: AI fetches attacker content. The AI service, operating as designed, retrieves the URL. To enterprise security tools, this looks like normal AI service traffic. The request originates from a trusted cloud service, not the compromised endpoint.
Step 3: Attacker embeds commands in responses. The attacker-controlled webpage includes new instructions for the malware. These might be encoded in metadata, hidden in HTML comments, or embedded in seemingly innocuous text that the AI summarizes.
Step 4: Malware receives instructions. The AI returns its summary to the compromised system. The malware extracts the hidden commands and executes them. The cycle repeats, creating persistent command-and-control capability.
The critical advantage: No direct connection between the compromised system and the attacker’s server ever occurs. The AI service acts as an unwitting proxy. Firewall logs show only traffic to trusted AI platforms. Intrusion detection systems see nothing unusual.
Enterprise security architectures are built on layers of defense. Firewalls, intrusion detection, endpoint protection, network segmentation, zero-trust principles. Yet this attack technique bypasses all of them simultaneously. Understanding why reveals fundamental assumptions in how we approach security.
Most organizations whitelist traffic to major AI service providers. Blocking these services would cripple productivity for knowledge workers who rely on AI assistants for research, writing, coding, and analysis. Security teams face an impossible choice: enable productivity or block a potential attack vector.
The reality is that blocking is not feasible. AI tools have become embedded in enterprise workflows. The attack exploits this economic and operational necessity.
All communication with AI services occurs over HTTPS. While this protects user privacy and data integrity, it also prevents security tools from inspecting the actual content of prompts and responses. Deep packet inspection sees only encrypted tunnels to trusted endpoints.
An attacker’s command embedded in an AI response looks identical to a legitimate productivity query. There is no signature to match, no pattern to detect.
Anomaly detection systems flag unusual behavior. But what defines unusual when AI tool usage varies dramatically across employees? Some users make hundreds of AI queries daily. Others make a handful. There is no baseline pattern to enforce.
Malware using AI for command-and-control can space out its queries, vary their timing, and mimic human usage patterns. The traffic volume is negligible compared to legitimate AI usage. It disappears into the noise.
The fundamental problem: Security tools are designed to identify malicious traffic. This attack uses traffic that is, by every measurable criterion, legitimate. The AI service is not compromised. The traffic pattern is not suspicious. The destination is trusted. Traditional security controls have no basis for intervention.
The discovery of AI-proxied command-and-control channels represents more than a novel attack technique. It highlights a broader challenge facing security teams: the tools that enable productivity also expand the attack surface in ways that are difficult to secure.
Consider an attacker who has compromised a system containing sensitive intellectual property, financial data, or customer information. Traditional data loss prevention tools monitor for large file transfers, unusual database queries, or attempts to access cloud storage.
But what if the attacker exfiltrates data by asking an AI assistant to summarize documents, with those documents hosted on attacker-controlled servers that encode the stolen data in their HTML? The AI fetches the content, processes it, and returns a summary. To security tools, this appears as normal AI usage.
Gigabytes of data can be exfiltrated in small chunks over time, each transmission appearing as a legitimate productivity task. No alarm triggers. No investigation launches. The theft remains invisible until the damage is discovered through other means.
Once an attacker establishes an AI-proxied command channel, they gain persistent access that is extraordinarily difficult to disrupt. Even if security teams discover and remove the initial malware, the attacker can reinstall it using the same covert channel.
The technique also enables lateral movement. An attacker who compromises one system can use AI-proxied communication to coordinate with malware on other systems within the network. Each compromised endpoint appears to be independently using AI tools for legitimate purposes.
The distributed nature of the attack makes containment challenging. Isolating individual systems does not sever the command channel. The attacker maintains communication as long as the compromised systems can reach AI services, which in most enterprises they always can.
Beyond serving as communication infrastructure, the same technique enables attackers to leverage AI capabilities for their operations. Malware can ask the AI to generate reconnaissance scripts, analyze system configurations, identify valuable targets, and suggest attack paths.
This creates a force multiplier effect. A less sophisticated attacker with limited technical skills can deploy malware that dynamically adapts its behavior based on AI-generated guidance. The barrier to entry for advanced persistent threats drops significantly.
While challenging, defending against AI-proxied command-and-control is not impossible. It requires rethinking assumptions about trusted traffic and implementing layered detection strategies that focus on behavior rather than signatures.
Monitor AI Service Usage Patterns
Establish baseline patterns for how employees use AI services. Track volume, timing, query types, and the domains AI services are asked to fetch. Deviations from normal patterns warrant investigation.
Red flags include:
Implement logging that captures metadata about AI service interactions. While you cannot decrypt the content, you can track who is using these services, when, and how frequently.
Attackers using AI-proxied command-and-control must host infrastructure somewhere. They register domains, set up servers, and create the web pages that AI services will fetch. This infrastructure leaves traces.
Proactive threat intelligence can identify these indicators before they are used against you:
This is where external threat monitoring platforms like Saptang Labs provide critical visibility. By continuously scanning for emerging threats and infrastructure patterns, you can identify potential C2 domains before your systems ever interact with them.
Focus on what happens on endpoints before and after AI service interactions. Malware must execute locally to craft prompts and process responses. This execution creates observable behavior.
Deploy endpoint detection and response tools that monitor for suspicious process behavior, unusual network patterns, and anomalous system calls. While the AI communication may be invisible, the malware orchestrating it is not.
Look for processes that programmatically interact with AI services, especially if those processes lack legitimate business justification or operate in ways inconsistent with human usage patterns.
Do not assume all AI service usage is benign simply because the destination is trusted. Apply zero-trust principles.
Implement controls such as:
These controls create friction that makes AI-proxied C2 more difficult without completely blocking legitimate usage.
For Indian enterprises, the emergence of AI-proxied command-and-control techniques arrives at a particularly challenging moment. Organizations are rapidly adopting AI tools to remain competitive, while simultaneously facing increasing regulatory scrutiny over cybersecurity practices.
The RBI’s April 2026 cybersecurity framework explicitly mandates external threat monitoring and advanced threat detection capabilities. Organizations must demonstrate not just that they have security controls, but that those controls address emerging attack vectors.
AI adoption in Indian enterprises is accelerating. Software development teams use AI coding assistants. Marketing teams leverage AI for content generation. Financial analysts employ AI for market research. Each of these use cases creates potential exposure if attackers exploit AI services as communication infrastructure.
The challenge for Indian CISOs is balancing productivity gains from AI tools against the expanded attack surface they create. Blocking AI services is not feasible in competitive markets. But failing to secure them adequately risks both breaches and regulatory non-compliance.
This is not a problem that can be solved with traditional security tools alone. It requires visibility into external threats, understanding of how attackers are evolving their techniques, and proactive defense strategies that anticipate rather than react to attacks.
Q1: Does this mean we should block AI services in our enterprise?
No. Blocking AI services would eliminate significant productivity benefits and is likely not sustainable as these tools become embedded in enterprise workflows. The better approach is implementing monitoring, anomaly detection, and zero-trust controls that allow legitimate usage while detecting malicious activity.
Q2: Are all AI assistants vulnerable to this technique?
Any AI service with web browsing capabilities can potentially be abused as a communication proxy. The technique exploits legitimate functionality rather than security vulnerabilities. However, some vendors have implemented controls to detect and prevent malicious usage patterns after security researchers disclosed the technique.
Q3: How do we know if our organization has been targeted using this method?
Detection requires analyzing patterns in AI service usage, monitoring for unusual domains being fetched by AI tools, and correlating endpoint behavior with AI interactions. Organizations should implement logging of AI service metadata and analyze it for anomalies. External threat intelligence can also identify known C2 infrastructure before it is used against you.
Q4: Does this technique require sophisticated attackers?
Initially, yes. Implementing AI-proxied C2 requires understanding both the AI service APIs and how to encode commands in ways that survive AI processing. However, as with most attack techniques, tools will emerge that automate the process. Over time, the barrier to entry will decrease significantly.
Q5: What should our immediate response be?
Start by gaining visibility into how AI services are being used in your environment. Implement logging for AI service interactions. Review which systems and users have access to AI platforms. Establish baseline usage patterns. Deploy external threat intelligence to identify emerging C2 infrastructure. Most importantly, treat AI service traffic with the same scrutiny as any other external service rather than assuming it is inherently safe.
How Saptang Labs Protects Against Evolving AI-Based Threats
The emergence of AI-proxied command-and-control highlights a fundamental challenge in modern cybersecurity: threats evolve faster than defenses can be deployed. Traditional security tools focus on detecting known attack patterns. But what happens when attackers leverage trusted infrastructure in novel ways?
This is where external threat intelligence becomes critical. Rather than waiting for attacks to reach your perimeter, proactive threat monitoring identifies malicious infrastructure and emerging techniques before they are weaponized against your organization.
Saptang Labs provides the external visibility enterprises need to stay ahead of AI-based threats:
Dark Web Monitoring
We track underground forums, Telegram channels, and cybercrime marketplaces where attackers discuss AI-proxied C2 techniques, share tools, and coordinate campaigns. When new attack methods emerge, our intelligence team identifies them early, allowing you to prepare defenses before attackers deploy them at scale.
Domain Threat Monitoring
Attackers using AI services as C2 proxies must host infrastructure somewhere. Our platform continuously monitors newly registered domains, identifies those with characteristics matching C2 infrastructure, and alerts you to potential threats before your systems interact with them. We analyze domain registration patterns, hosting choices, and content structures that indicate malicious intent.
Credential Threat Monitoring
AI-proxied attacks often begin with compromised credentials. We monitor for corporate email addresses, employee credentials, and system accounts appearing in breach databases, credential dumps, and underground markets. Early detection allows you to rotate credentials before attackers exploit them to establish AI-based C2 channels.
Social Media and Public Intelligence
Threat actors often test techniques and share proof-of-concepts on social media before deploying them operationally. Our monitoring covers technical forums, security research publications, and hacker communities where AI exploitation methods are discussed. This provides early warning of emerging threats.
Why External Threat Intelligence Matters
Internal security controls are necessary but not sufficient. They detect attacks after they reach your infrastructure. External threat intelligence provides advance warning, allowing you to strengthen defenses before attackers target you.
For AI-proxied threats specifically, external intelligence is crucial because the attack traffic itself appears legitimate. You cannot rely on network security tools to detect it. Instead, you must identify the attacker infrastructure and techniques before they are deployed.
Saptang Labs continuously monitors the external threat landscape so you do not have to. Our platform aggregates intelligence from multiple sources, correlates indicators, and provides actionable alerts tailored to your specific threat profile. We help you understand not just what attacks are possible, but which ones are actively being developed and deployed against organizations like yours.
Ready to strengthen your defenses against AI-based threats?
Contact Saptang Labs today for a demonstration of how external threat intelligence can provide the visibility your security team needs to defend against evolving attack techniques. We serve enterprises across financial services, technology, healthcare, and government sectors with tailored threat intelligence that addresses your specific risk profile.
Visit saptanglabs.com or email sales@saptanglabs.com to schedule a consultation.