Inside the AI-Supply-Chain: How a Trusted Assistant Became the Breach Vector

A new class of cyber-attack has surfaced in the age of enterprise AI, and it is rewriting the data-governance rulebook. The stealthy exploit, dubbed Shadow Escape, reportedly allows bad actors to exfiltrate sensitive personal and organizational data via standard AI assistant workflows — even when all systems appear to be operating inside trusted boundaries.

The vulnerability was uncovered by the security research team at Operant AI, which characterizes the attack as a zero-click chain that exploits the trusted infrastructure of the recently emerging Model Context Protocol (MCP) standard for AI agents. According to Operant’s analysis, the exploit doesn’t rely on phishing, malicious extensions or human error — instead, it piggy-backs on legitimate agent identities that are already authorized inside the enterprise stack.

“The Shadow Escape attack demonstrates the absolute criticality of securing MCP and agentic identities. Operant AI’s ability to detect and block these types of attacks in real-time and redact critical data before it crosses unknown and unwanted boundaries is pivotal to operationalizing MCP in any environment, especially in industries that have to follow the highest security standards,”said Donna Dodson, the former head of cybersecurity at NIST.

What exactly is Shadow Escape?

In essence, the attack begins with what appears to be a legitimate document—say, an onboarding PDF or instruction manual that is uploaded into an AI-assistant environment (for example inside a system like ChatGPT, Claude, or Gemini) with access through an MCP connector. Once the agent has access to internal systems (databases, file shares, APIs) via MCP, hidden instructions embedded in the document can direct the agent to:

• discover and pull private records (e.g., SSNs, medical record numbers, banking data),

• aggregate them across systems the user may not explicitly know exist,

• then use the same agent to send the aggregated data to a malicious external endpoint—all without the user’s awareness.

Importantly, this exfiltration happens inside the fortress: inside the enterprise firewall and under an identity the organization has deemed trustworthy. Because the agent is authorized via normal channels, traditional DLP (data-loss prevention) and firewall logs may not flag the activity. The trust layer is exactly what the attacker exploits.

The attack is broad. Operant’s researchers note the technique is platform-agnostic, targeting “any AI agent using MCP to connect to databases, file systems, or external APIs.” The report suggests the scale is staggering: “Because Shadow Escape is easily perpetrated through standard MCP setups and default MCP permissioning, the scale of private consumer and user records being exfiltrated … could easily be in the trillions.”

Why is MCP the new battleground?

The Model Context Protocol (MCP) has become a foundational standard for agentic AI workflows. In short, it enables AI assistants to discover tools, access data sources, execute actions, and propagate context across systems—often spanning multiple platforms and enterprise domains.

With its increased adoption in 2024-25, including by major vendors, MCP has grown as the “connective tissue” between LLMs and enterprise infrastructure. What was once isolated (chatbot + siloed data) is now fully integrated (chatbot + database + API + file system). That creates enormous productivity gains—and equally enormous risk.

By abusing MCP, the attacker turns the trusted assistant into the threat vector. The technique doesn’t depend on vectoring through the user—it subverts the identity that has already been given trust. One expert, Roger Grimes (CISO Advisor at KnowBe4), contextualizes it this way:

“I’m familiar with at least one other similar attack involving another, more popular AI tool, that the research plans to publicly release soon after practicing responsible disclosure with the vendor. They seem to be coming out of the woodwork so to speak. This zero-click attack is just going to be one of thousands coming out over the next few years. These initial reports are just the beginning stages of what promises to be years and years of new types of exploits. That's because AI and the way they interact with other AIs and humans are just starting to be discovered and explored. The sheer amount of ways that any AI can interact with something else makes it far harder, if not impossible, for the vendor or a cyber defender to test before the AI is released.

“We didn't do a great job at testing non-AI, more deterministic software and systems, to make sure they didn't have vulnerabilities. Heck, we had over 40K separate publicly announced vulnerabilities last year and we are on our way to having over 47K this year. Non-deterministic AIs with the ability to have thousands of different types of interactions is just going to make that number explode. We are just now opening Pandora’s box, and we are definitely not going to like what we see. I thought stuff was complex in the past. We will think of the past decades of vulnerabilities as the ‘good times’ before AI everywhere arrived. It’s getting ready to be very stormy.”

The technical anatomy: how an attack unfolds

1. Upload/ingestion: A “normal” document—e.g., a service manual or onboarding PDF—is uploaded into the assistant.

   
   

2. Discovery & aggregation: The assistant, via MCP tools, queries CRM systems, databases, file shares, and other enterprise sources—even those the human user didn’t know about.

   
   

3. Hidden instruction: Embedded within the document are invisible or obfuscated instructions that tell the assistant to exfiltrate.

   
   

4. Exfiltration: Using the agent’s tool access (HTTP request, data API), the assistant silently sends large amounts of sensitive data to an attacker-controlled endpoint.

   
   

5. Persistence/escape: Since the agent is operating under trusted credentials inside the firewall, forensic logs may look benign (e.g., internal queries) and miss the outbound exfiltration

   
   

Because every step happens via authenticated, authorized channels, this exploit is invisible to many conventional controls.

Why current tools struggle to defend

Standard perimeter defenses, network firewalls, and DLP systems operate on the assumption that malicious access comes from outside or via abnormal network channels. But in this scenario:

• The identity is trusted.

• The traffic is encrypted and legitimate (e.g., via HTTPS).

• The document asset appears benign.

• The tool invocation is allowed.

• The data flow looks normal until it’s too late.

As one commentary put it: “The next data breach won’t come from a hacker, it will come from a trusted AI agent.” The message is stark: organizations must shift from perimeter-first to agent-runtime-first thinking.

What organizations must do now

According to the Operant research, the roadmap to mitigation involves embedding security at the runtime layer of AI agent workflows — not just bolting on traditional controls afterwards. The recommended steps include:

• Contextual IAM for AI agents: Dynamic permissioning based on agent context, tool usage, data type, and trust score.

• Document validation/sanitization: Scan and sanitize uploaded assets for hidden instructions or anomalies before they reach the agent runtime.

• Real-time tool invocation monitoring: Log and audit tool calls, flag anomalous sequences (e.g., unexpected external HTTP calls following data-dense queries).

• Inline redaction and tokenisation: Prevent sensitive data from leaving the agent boundary unmasked or in the clear.

• MCP supply-chain governance: Catalog all MCP clients/servers, apply trust scores to tool endpoints, and isolate or quarantine untrusted tool integrations.

• Runtime observability & forensics: Capture behavioral telemetry for agents, conduct red-team simulations, and be prepared to respond within seconds.

These strategies are part of what Operant’s platform claims to deliver.

The broader implications: an enterprise-scale wake-up call

With the rapid enterprise adoption of agentic AI (in sectors from healthcare to finance, legal to retail), the stakes are exceptionally high. The threat model here isn’t a lone disgruntled insider; it’s a system authorized at scale, pulling from thousands or millions of records, then exfiltrating via its own granted tools.

For example:

• A healthcare service assistant connected via MCP queries EMR systems, billing systems and identity-management tables.

• A banking AI agent uses MCP connectors to query transaction archives, KYC databases, account ledgers and internal risk tools.

• A legal-sector AI accesses case-management files, privileged-client communications and settlement data via MCP-enabled tools.

Given these flows, one compromised document (uploaded to a wide audience) could initiate exfiltration across hundreds of organizations using the same agent blueprint. The blast radius is extreme.

Final thoughts

Shadow Escape represents a textbook example of how digital trust assumptions can be weaponized. The architecture that made agentic AI so powerful — seamless access, dynamic tool invocation, shared context — is now the same architecture adversaries are exploiting.

For organizations deploying AI assistants today, the question is no longer if they’ll face an AI-based threat, but when. And whether they’ll detect it before irreversible damage.

When the agent becomes the adversary, the defensive posture must follow suit: full-lifecycle visibility, runtime enforcement, context-aware tool governance and continuous observability. Without those, the “helpful assistant” could turn into the biggest insider threat your organization never saw.