Injection by Deception: How Fraudsters Are Hijacking Identity Verification Flows with Injection Attacks

Most breaches are designed to break down the “front door” in order to enter an organization’s environment. But injection attacks don’t present in the same, aggressive way because they quietly slip through the side, undetected. These advanced exploits manipulate legitimate identity verification flows, and in the process, they insert malicious data at critical points. Their goal is to bypass verification safeguards and assume a trusted identity. As fraudsters become more sophisticated — and as organizations rely more heavily on automated identity verification systems — understanding how these attacks work, and how to stop them, is critical to protecting the integrity of your onboarding and risk strategies.

If your identity verification system accepts user-supplied data or integrates third-party components without validating how information is entered, your infrastructure has the potential to be exposed to injection attacks. These vulnerabilities can surface in the onboarding phase and throughout the entire customer lifecycle. 

At Prove, we’ve seen how critical it is to establish trust at the first point of interaction — and to maintain that trust continuously. In this blog, we’ll examine how injection attacks work, why they’re gaining traction, and what steps you can take to harden your systems. Whether you’re building from scratch or modernizing legacy workflows, the goal is the same: stop fraudsters from hijacking your identity verification process.

What Is an Injection Attack?

An injection attack is when a bad actor associates a phone number with a victim's identity to impersonate them. It’s a fraud attack type that exploits weak data cleansing practices in production, bank-grade data sources. Ultimately, it describes a scenario when a bad actor has leveraged ways to undermine production systems to fraudulently associate a phone to a person for the purposes of trying to impersonate that victim for financial gain (although it does not include targeting a previous owner of a phone).

These attacks most often occur when a bad actor activates a phone number of any line type (most often mobile) and then purposefully and wrongfully associates that phone number to one or many victims through credit or utility record methods. They then attempt to impersonate those victims through phone verification. Phone numbers may infrequently be "injected" and wrongfully associated to unsuspecting consumers often following a legitimate phone number lifecycle event — such as a recent reassignment, port, or reconnection after non-payment. These events often result in a phone number appearing short-tenured, which is a key indicator Prove looks for.

“The attacker isn’t trying to break through security; they’re manipulating the process itself…”

It is becoming a more common approach used to commit identity theft at account opening but can also be used to associate a phone to a synthetic identity profile. In some cases, it can also be used at account takeover.

Unlike social engineering or credential theft, this form of fraud happens in-system. The attacker isn’t trying to break through security; they’re manipulating the process itself to pass as legitimate.

These attacks can take several forms:

• Form injection: Pre-populating identity verification fields with synthetic or stolen data before a user ever touches the application.
• Session hijacking or replay attacks: Injecting a previously successful session token into a new verification attempt.
• Bypassing front-end controls: Using scripts or browser dev tools to trick the system into accepting false positives or skipping key steps.

And that’s precisely what makes injection attacks so dangerous. They don’t rely on brute force or guesswork; they exploit logic gaps, implicit trust, and automation blind spots within your identity infrastructure. These vulnerabilities are often invisible until it’s too late — when fraud has already occurred at scale, under the guise of verified legitimacy.

Why Injection Attacks Are on the Rise

Injection attacks are quickly becoming a preferred strategy for sophisticated fraudsters because their tactics use a perfect storm of technological, behavioral, and operational trends that are transforming how identity is verified online.

First, the growing reliance on automation in digital identity verification has created a larger attack surface. Organizations are streamlining onboarding and KYC processes to improve user experience, reduce operational costs, and scale rapidly. But in doing so, many are reducing or even removing human oversight. That efficiency creates opportunity: attackers know that if they can craft inputs that look legitimate to a machine, there’s a good chance they’ll be waved through without scrutiny.

“...the growing reliance on automation in digital identity verification has created a larger attack surface.”

At the same time, the tools required to carry out these attacks have become more accessible. Injection frameworks, browser automation scripts, and even jailbreak plugins for identity verification platforms are now widely available on the dark web or underground marketplaces. And they aren't built just for elite hackers anymore — they’re packaged as ready-to-deploy fraud kits, which democratizes their use and makes them available to almost anyone who wants to launch scalable campaigns.

The rise of generative AI has only accelerated the problem. Fraudsters can now easily produce high-quality synthetic documents, photorealistic faces, and even voice clips that can pass basic verification tests. But possession of those types of assets is only part of the equation — injection is how they deploy them at scale. Instead of trying to fake an ID or spoof a webcam live, attackers inject the data directly into the flow, which bypasses the need for real-time interaction entirely.

There’s also a more subtle factor at play: overconfidence and too much implicit trust in system logic. Many digital identity flows are designed with the assumption that if a user reaches a certain step — like uploading a document or passing a facial scan — they’ve already cleared necessary risk checks. Injection attacks exploit that logic, inserting synthetic data into "trusted" parts of the process where few additional controls exist. This misplaced trust is exactly what fraudsters rely on.

What we’re seeing is a fundamental shift in the nature of fraud. It’s no longer purely opportunistic; it’s calculated, scalable, and increasingly invisible. Injection attacks are a prime example of this evolution — they trick systems, but they do it by exploiting the very trust and automation those systems were built on.

What Makes Identity Verification So Vulnerable?

Most identity verification systems were designed to stop obvious manipulation — bad photos, mismatched names, reused IPs. But injection attacks are stealthy. They target assumptions built into the flow, such as:

• Trusting data passed via APIs or iframes without validating its source
• Over-relying on front-end controls to gate access
• Failing to correlate behavioral, device, and environmental signals in real time
• Insufficient checks for short phone number ownership tenure, which is a critical indicator of an injection attack.

Fraudsters know this. They're exploiting the seams between security layers, often in ways that don’t trigger traditional alarms

Anatomy of an Injection Attack

Understanding how an injection attack works — from setup to execution — is key to stopping it. These are carefully orchestrated sequences that take advantage of trust gaps in digital identity verification flows, and those gaps show up at various points along the process.

Here’s a breakdown of how a typical injection attack unfolds:

Step 1: Reconnaissance and Targeting: Before launching the attack, the fraudster studies the target system. They sign up as a normal user (or scrape documentation) to understand:

• What data fields are required for identity verification
• Which third-party services are being used (e.g., document verification, facial recognition)
• How and when data is submitted (form structure, API endpoints, iframe behavior)

The fraudster is looking for weak spots — particularly where the system blindly trusts the data being passed to it, and how they can leverage weak data cleansing practices to wrongfully associate a phone number with a victim's identity.

Step 2: Scripted Injection Setup: With the flow mapped out, the attacker builds a custom script or uses a fraud-as-a-service toolkit to automate the injection. This may involve:

• Bypassing front-end controls, such as required fields, format restrictions, or upload validations
• Pre-filling forms with synthetic or stolen PII (personally identifiable information)
• Injecting a valid-looking document or selfie, often AI-generated, directly into the verification endpoint
• It’s important to understand that the attacker aims to associate a phone number they control with the victim's identity, which directly exploits instances where phone numbers have recently been reassigned, ported, or reconnected after non-payment.

Note that the interaction doesn’t play out with the UI like a human would — they manipulate it at the code or API level to gain efficiency and scale.

“These are carefully orchestrated sequences that take advantage of trust gaps in digital identity verification flows…”

Step 3: Identity Verification Subversion: This is where the deception happens. The goal is to trick the system into wrongfully associating their controlled phone number with the victim's identity, making it appear legitimate. The attacker does the following:

• Submits the injected data via an automated call or hijacked session.
• Exploits timing gaps or race conditions to bypass liveness checks or verification steps.
• Leverages browser tools, proxies, or headless browsers to mask device and network signals.

If the system isn’t validating how the data is submitted — only what is submitted — the attack succeeds.

Step 4: Access Granted: The fraudster now holds a verified account under a synthetic or stolen identity, linked to their controlled phone number. Depending on the goal, they may:

• Open a bank or fintech account to launder money
• Execute a SIM swap to hijack someone’s phone number
• Access a high-value service (e.g., loans, crypto wallets, healthcare benefits)

From the system’s point of view, everything looks legitimate. The right data was submitted, the checks passed, and there were no overt red flags.

Step 5: Scale and Repeat: Once the method works, attackers scale it. They initiate the following steps:

• Deploy the attack across dozens or hundreds of verification attempts
• Rotate IP addresses, devices, and synthetic identities to stay under the radar
• Refine the attack in real time to avoid new detection rules or friction points
• Continuously exploit the vulnerabilities that allow them to associate their phone numbers with new victim identities.

In many cases, injection attacks aren’t isolated — they’re part of large-scale fraud rings using automation and AI to operate like shadow tech startups..

How Prove Helps Organizations Combat Injection Attacks

At Prove, we understand that combating injection attacks requires a sophisticated approach that goes beyond traditional identity verification. Our strategy is deeply rooted in preventing the fraudulent association of a phone number with a victim's identity.

Prove's Global Fraud Policy (GFP) is specifically designed to protect against injection attacks by identifying key indicators. We leverage real-time phone intelligence to:

• Check for short ownership tenure (OV, OS): This is a primary indicator of an injection attack. When a phone number has only recently been associated with an identity, especially after a recent reassignment, port, or reconnection after non-payment, it raises a significant red flag. The GFP flags these "short-tenured" phone numbers as suspicious.
• Monitor for concurrent short tenures (C2, C3, etc.): Fraudsters often attempt to inject the same phone number, or a small pool of phone numbers, across multiple victim identities in a short period. The GFP identifies patterns of concurrent short tenures, indicating a coordinated injection attack.

By focusing on the integrity of the phone number's association with an identity, Prove offers a crucial layer of defense. Instead of just verifying what data is presented, we verify the provenance and stability of the phone-identity link. This allows organizations to:

• Establish trust at the first point of interaction: By instantly assessing the risk of a phone number being fraudulently injected at account opening or registration.
• Prevent identity theft and synthetic identity fraud: By flagging attempts to link a bad actor's phone number to a legitimate victim or a fabricated profile.
• Enhance existing fraud prevention strategies: Providing a unique signal that complements other identity verification methods and catches attacks that often slip through traditional controls.

Fraudsters are becoming increasingly sophisticated, and Prove provides the critical intelligence needed to detect and prevent injection attacks before they lead to significant financial loss and reputational damage. By focusing on the fraudulent association of phone numbers, we empower organizations to harden their systems and stop fraudsters from hijacking their identity verification processes.

Learn more about how Prove is helping organizations prevent injection attacks and other breaches.