CVE-2026-57476 in AI Assist for Customer
Summary
by MITRE • 07/10/2026
Deloitte AI Assist for Customer exposed unauthenticated API endpoints that allowed an attacker with knowledge of additional parameters to read from or inject content into the retrieval-augmented generation (RAG) corpus. On 2026-03-25, AI Assist for Customer restricted network access and enforced authentication for the previously exposed endpoints.
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Analysis
by VulDB Data Team • 07/10/2026
The vulnerability in Deloitte AI Assist for Customer represents a critical security flaw that exposed unauthenticated API endpoints within a retrieval-augmented generation system. This system architecture typically relies on a corpus of documents or data that is indexed and used to generate responses to user queries, making it particularly sensitive to unauthorized access or manipulation. The exposure occurred due to insufficient access controls and authentication mechanisms that allowed attackers with knowledge of specific endpoint parameters to perform read operations or inject malicious content into the underlying RAG corpus.
The technical implementation of this vulnerability aligns with common weaknesses found in API security frameworks, specifically those related to improper access control mechanisms and lack of input validation. The system's failure to enforce proper authentication protocols meant that any attacker who could deduce or discover the correct API endpoint parameters could exploit the system to extract sensitive information from the corpus or introduce malicious data that would influence the AI's response generation capabilities. This represents a fundamental breakdown in the security model of the system, as it allowed for both data exfiltration and data injection attacks through a single vulnerable interface.
The operational impact of this vulnerability extends beyond simple unauthorized access, as it fundamentally compromised the integrity and confidentiality of the RAG system's underlying data. Attackers could potentially gain insights into sensitive customer information, proprietary business data, or confidential communications that were stored within the AI corpus. The ability to inject content into the system could lead to misinformation campaigns, manipulation of AI responses, or even the introduction of malicious prompts that would affect downstream applications using the AI Assist service. This type of vulnerability directly impacts the trustworthiness of AI-generated outputs and can have cascading effects throughout an organization's digital ecosystem.
The remediation implemented by Deloitte on 2026-03-25 involved restricting network access and enforcing authentication for previously exposed endpoints, which aligns with standard security best practices for API protection. This solution addresses the core issue of insufficient access controls and demonstrates proper incident response procedures. The fix likely included implementing proper authentication mechanisms such as API keys, OAuth tokens, or other secure authentication protocols to ensure that only authorized entities could access the RAG corpus through the API endpoints. Additionally, network-level restrictions would have limited access based on IP addresses or network segments, reducing the attack surface and preventing unauthorized access from external or internal sources.
This vulnerability scenario reflects patterns commonly observed in AI security incidents where the complexity of modern AI systems introduces additional attack surfaces beyond traditional application security concerns. The exposure highlights the need for comprehensive security testing that includes API security assessments, proper access control implementation, and regular security audits of AI systems. Organizations implementing RAG-based solutions should consider the principles outlined in the CWE database under categories such as CWE-284 (Improper Access Control) and CWE-352 (Cross-Site Request Forgery) when designing their security frameworks. The incident also relates to ATT&CK techniques involving credential access and privilege escalation, particularly when considering how attackers might leverage exposed API endpoints to gain deeper system access or manipulate AI training data.
The broader implications of this vulnerability extend to the entire field of AI security where organizations must consider not just the traditional security boundaries of their applications but also the unique risks introduced by AI systems that process and generate content based on large datasets. The exposure demonstrates the importance of implementing zero-trust security models even for internal AI systems, as unauthorized access to training data or corpus information can compromise the entire AI system's reliability and trustworthiness. Organizations should implement proper monitoring and logging of API access patterns to detect anomalous behavior that might indicate exploitation attempts against similar vulnerabilities in their own AI implementations.