Financial services vulnerabilities in 2024 reveal persistent identity gaps

At a glance

What this is: INTIGRITI’s review of financial services vulnerabilities shows that information disclosure and injection remain common, while ransomware and AI-enabled fraud are shaping 2025 risk planning.

Why it matters: For IAM practitioners, the article matters because many of the failure modes begin with weak authentication, over-broad access, and poor authorization boundaries around applications, APIs, and customer data.

By the numbers:

• The financial services industry’s average data breach cost rose to $6.08 million in 2024, up from $5.90 million in 2023.
• Nearly 1 in 4 financial businesses have fallen victim to a data breach.
• LoanDepot’s 2024 breach exposed the personal information of nearly 17 million customers.
• The Ghost ransomware group attacked financial institutions across over 70 countries.

👉 Read INTIGRITI’s analysis of 2024 finance-sector vulnerabilities and 2025 threats

Context

Financial services faces a mix of technical and governance failures because exposed data, weak authorization, and credential abuse often begin in ordinary application flows rather than sophisticated exploits. The primary identity security question is not only whether a system is patched, but whether access boundaries, API controls, and customer-facing workflows prevent a small input change from becoming a data exposure event.

In this article’s finance-sector context, those weaknesses intersect directly with IAM, PAM, and identity verification because sensitive transactions, customer records, and support tooling are all gated by trust decisions. The article is typical of the sector: the attack surface is broad, the incentives are high, and minor access-control mistakes can produce outsized impact.

Key questions

Q: What breaks when finance applications have weak object-level authorization?

A: Attackers can change an identifier or request parameter and retrieve another user’s data, transaction history, or account details. In financial environments, that often means the application looks functional while silently exposing sensitive records. The real failure is not the input change itself, but the absence of server-side authorization on every sensitive object and transaction.

Q: Why do injection flaws create outsized risk in financial services?

A: Because finance systems often connect customer workflows, support tools, and privileged backends. If untrusted input reaches a command, query, or template engine, an attacker may move from one request to administrative access or data extraction. The risk rises sharply when privileged remote access products sit in the same trust path.

Q: How should security teams stop deepfake impersonation from bypassing identity proofing?

A: Teams should combine liveness detection, document validation, device intelligence, and risk-based step-up checks at the points where attackers gain the most value. The goal is not perfect recognition of synthetic media, but reducing the chance that a single spoofed interaction can create durable trust.

Q: Who is accountable when account takeover and synthetic identity fraud occur?

A: Accountability usually sits across fraud, IAM, security, and product teams because the failure spans onboarding, session trust, and action-level controls. In practice, the owner should be the team that can change the decision point where abuse becomes possible. Shared risk does not mean shared inaction.

Technical breakdown

Information disclosure in financial apps: why weak authorization leaks data

Information disclosure happens when a system reveals data to a user or process that should not see it. In finance, this often appears as broken object-level authorization, insecure API design, or insufficient server-side checks that let attackers change an identifier and retrieve someone else’s records. The flaw is rarely advanced exploitation. It is usually a trust decision made too early in the request path, before the application verifies who the caller is and what they are allowed to access. That makes the weakness both common and high impact, especially where customer records, transactions, and support workflows share the same backend objects. Practical implication: enforce server-side authorization on every sensitive object and transaction path.

Practical implication: enforce server-side authorization on every sensitive object and transaction path.

Injection flaws and privileged remote access: how input becomes control

Injection vulnerabilities occur when untrusted input is treated as executable logic, whether that means SQL, templates, commands, or backend request parameters. In the financial examples discussed here, the concern is not just data theft but privilege expansion, because a single injected command can pivot into internal systems or privileged support tooling. When injection lands in remote access products or internal administrative workflows, it can bypass normal user boundaries and expose data that should never be reachable from the original request. Practical implication: treat admin and support paths as high-risk attack surfaces and validate inputs at every trust boundary.

Practical implication: treat admin and support paths as high-risk attack surfaces and validate inputs at every trust boundary.

AI-driven social engineering and synthetic identity: the next finance fraud layer

Generative AI changes fraud by making deception cheaper, more personalised, and harder to distinguish from legitimate communication. Voice cloning, synthetic video, and convincing phishing content reduce the cues users and service desks have traditionally relied on. For financial institutions, the identity challenge is bigger than email defence, because attackers can now impersonate customers, executives, vendors, and even internal staff with enough realism to trigger account reset, payment, or access decisions. That pushes identity verification, step-up controls, and call-centre assurance into the same risk category as traditional cyber controls. Practical implication: harden identity verification workflows before synthetic impersonation becomes routine.

Practical implication: harden identity verification workflows before synthetic impersonation becomes routine.

Threat narrative

Attacker objective: The attacker wants to turn a small trust failure into broad financial data exposure, unauthorized access, or monetizable fraud.

1. Entry begins with information disclosure, injection, or AI-assisted social engineering that gives an attacker a foothold in a finance workflow or support channel.
2. Escalation follows when weak authorization, over-privileged access, or compromised support tooling lets the attacker move from a single request to broader data or administrative control.
3. Impact is theft of customer data, disruption through ransomware, or fraudulent transaction activity that damages trust and increases recovery cost.

NHI Mgmt Group analysis

Identity control failures, not just application bugs, are what turn finance-sector vulnerabilities into breach events. The article’s examples repeatedly show that unauthorized access, not simply malformed input, is the point where technical flaws become business risk. That is an IAM and PAM problem as much as an application security problem, because authorization checks, support access, and transaction permissions define the real boundary. Practitioners should read these vulnerabilities as governance failures around access, not isolated code defects.

AI-driven social engineering creates a verification trust gap that standard fraud controls are not yet designed to absorb. Synthetic identity tactics lower the cost of impersonation and increase the probability that humans, service desks, and workflow approvals will trust the wrong signal. This matters because finance teams often still rely on cues like voice, familiarity, or email context. The field needs stronger identity verification patterns that treat persona quality as a security control, not a user-experience detail.

Information disclosure in finance is a named concept worth tracking as a governance category. It is the pattern where small authorization errors, such as object tampering or weak API enforcement, expose high-value records without a dramatic exploit chain. That pattern is especially dangerous in regulated sectors because it can coexist with compliance and still leak sensitive data. NHI Mgmt Group’s position is that exposure control, not just vulnerability volume, should anchor prioritization.

Ransomware resilience in financial services now depends on reducing identity blast radius before a crisis begins. The article’s ransomware discussion reinforces that backup strategy alone is not enough when attackers can reach privileged paths, third-party channels, or critical supplier dependencies. Identity governance matters here because privileged access and standing trust often decide whether ransomware becomes a local incident or a sector-wide disruption. Practitioners should focus on containment boundaries, not only recovery speed.

What this signals

Financial institutions should treat application-layer disclosure, injection, and synthetic fraud as a single governance problem rather than separate security queues. The control objective is to reduce trust in any workflow that can expose records, approve payments, or trigger support actions without robust verification. For identity teams, that means tighter linkage between IAM policy, customer verification, and privileged support access, supported by guidance such as NIST SP 800-63 Digital Identity Guidelines.

Verification trust gap: this is the point where organisations trust a human signal, a support process, or a customer workflow more than the evidence behind it. In finance, that gap is widening because AI can now mimic legitimate identity cues at scale. Programmes that still depend on static knowledge checks or informal approvals will need stronger step-up logic and auditability before 2025 threat patterns mature further.

Attackers do not need novel exploits when one broken authorization decision can expose enough data to support fraud, extortion, or lateral movement. Teams should therefore measure not just vulnerability counts, but how often sensitive workflows are protected by explicit authorization, verified identity, and constrained access paths.

For practitioners

• Tighten object-level authorization on finance workflows Review every customer-facing and internal API that returns account, transaction, or support data. Enforce server-side checks on each object request rather than trusting client-supplied identifiers or hidden parameters. This is where simple tampering becomes unauthorised disclosure.
• Separate privileged support access from normal application paths Move remote support, admin, and break-glass functions into distinct authentication and authorization flows with stronger logging and approval. Injection in a support platform is far more damaging when the same path can reach sensitive data and operational controls.
• Harden identity verification against synthetic fraud Add step-up checks for payment changes, account recovery, and high-risk service desk actions. Use multi-factor verification, call-back validation, and out-of-band confirmation where synthetic voice or video could influence decisions.
• Reduce blast radius with least-privilege access reviews Reassess who can reach customer records, financial logs, and privileged tooling. Remove standing access from service accounts and third parties wherever the business process allows, because over-broad access turns modest flaws into major incidents.

Key takeaways

• Finance-sector breaches still often begin with weak authorization and data exposure, not with advanced exploitation.
• The evidence points to material scale, including $6.08 million average breach cost and nearly 1 in 4 firms affected.
• Identity verification, privileged access, and API authorization are the controls most likely to shrink the attack path.

Key terms

• Information Disclosure: Information disclosure is a failure where a system reveals data to an unauthorised user or process. In financial services, the leak may come from broken object access, weak API checks, or misconfigured workflows that expose records without a dramatic exploit.
• Injection Vulnerability: An injection vulnerability occurs when untrusted input is interpreted as code, query logic, or executable instructions. In practice, this can let an attacker retrieve data, alter backend behaviour, or pivot into privileged systems if input handling is not tightly controlled.
• Synthetic Identity: A synthetic identity is a software-based actor that can authenticate, request access, and execute actions without being a human user. In practice, this includes AI agents, bots, service accounts, tokens, and other machine identities that need clear ownership, scope, and revocation.
• Step-Up Verification: Step-up verification is a stronger identity check applied when risk increases, such as during password reset, device change, or privileged access request. It uses higher-assurance signals than a static question, such as device possession, authenticated context, or approved administrative review.

What's in the full article

INTIGRITI’s full article covers the operational detail this post intentionally leaves for the source:

• Research-community examples of how small request changes exposed transaction and account data.
• More detail on the command injection case affecting privileged remote access tooling and what made it exploitable.
• Sector-specific recommendations for hardening financial applications against disclosure and injection flaws.
• The article’s 2025 watchlist for AI-driven social engineering, ransomware evolution, and synthetic identity risk.

👉 INTIGRITI’s full article adds the researcher findings, incident examples, and 2025 risk priorities.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, secrets management, and machine identity security. It helps practitioners connect identity controls to the broader access and risk decisions that shape resilient programmes.