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The post Apple OpenAI Lawsuit Escalates Over AI Trade Secrets appeared first on Daily CyberSecurity.
The Apple OpenAI lawsuit intensifies as Apple accuses a former engineer of using stolen trade secrets to train AI agents and destroying digital evidence.
A 26-year-old Canadian man once described as one of the most consequential cybercrime threat actors of 2024 has pleaded guilty to computer fraud and conspiracy to hack and extort more than 165 organizations that used the cloud provider Snowflake. Connor Riley Moucka, of Kitchener, Ontario, also admitted to stealing call and text history records of more than 100 million AT&T customers.
A surveillance photo of Connor Riley Moucka, a.k.a. “Judische” and “Waifu,” dated Oct 21, 2024, 9 days befor
A 26-year-old Canadian man once described as one of the most consequential cybercrime threat actors of 2024 has pleaded guilty to computer fraud and conspiracy to hack and extort more than 165 organizations that used the cloud provider Snowflake. Connor Riley Moucka, of Kitchener, Ontario, also admitted to stealing call and text history records of more than 100 million AT&T customers.
A surveillance photo of Connor Riley Moucka, a.k.a. “Judische” and “Waifu,” dated Oct 21, 2024, 9 days before Moucka’s arrest. This image was included in an affidavit filed by an investigator with the Royal Canadian Mounted Police (RCMP).
The U.S. Justice Department said between February and October 2024, Moucka and co-conspirators used stolen login credentials to steal cloud-hosted data belonging to at least 165 customers of a U.S.-based software-as-a-service company.
The hackers targeted stolen credentials for Snowflake customer accounts that did not enforce multi-factor authentication, and extorted or attempted to extort a host of well-known companies, including TicketMaster, Lending Tree, Advance Auto Parts and Neiman Marcus. Snowflake responded to the data thefts by increasing password complexity requirements and enforcing multi-factor authentication.
Moucka adopted new nicknames frequently — sometimes operating multiple identities concurrently — but two of his best-known monikers were “Judische” and “Waifu.” Judische’s admitted role in the Snowflake data thefts was first documented by KrebsOnSecurity in a September 2024 story about the overlap between Western, English-speaking cybercriminals and extremist groups that harass and extort minors into harming themselves or others.
That September 2024 story identified Judische as a software engineer from Ontario who has been involved in numerous data breaches and voice phishing attacks against U.S. companies since at least 2020. A little more than a month later, Canadian authorities arrested Moucka on a provisional warrant from the United States.
The government says Moucka and others used their unauthorized access to steal billions of sensitive customer records and download terabytes of information, “including individuals’ non-content call and text history records, banking and other financial information, payroll records, Drug Enforcement Administration (DEA) registration numbers, driver’s license numbers, passport numbers, social security numbers and other personally identifiable information. They then extorted victims by threatening to publish data online.”
Moucka also threatened and harassed government officials and security researchers who were helping to track him down. The Justice Department said the conspirators made over $2.5 million in ransom payments, and that in at least one instance, Moucka re-extorted a victim with threats of further disclosure of the victim’s stolen data.
“Moucka used the stolen data of a government officer and members of a then-former government officer’s immediate family in this re-extortion attempt,” reads a statement from the Justice Department.
One of Moucka’s admitted co-conspirators is Cameron “Kiberphant0m” Wagenius, a U.S. Army soldier who pleaded guilty in July 2025 to extorting AT&T and Verizon for their customer account data. Less than a month before Wagenius’s arrest, KrebsOnSecurity published a deep dive into Kiberphant0m’s various Telegram and Discord identities over the years, revealing how the owner of the accounts told others they were in the Army and stationed in South Korea.
One of several selfies on the Facebook page of Cameron Wagenius.
Kiberphant0m also re-extorted victims. Immediately following Moucka’s arrest, Kiberphant0m posted on hacker forums what he claimed were the AT&T call logs for then President-elect Donald Trump and for then Vice President Kamala Harris, as well schematics allegedly stolen from the U.S. National Security Agency (NSA).
Wagenius is set to be sentenced on September 3, 2026. The government says he faces a maximum penalty of 20 years in prison for conspiracy to commit wire fraud, a maximum penalty of five years in prison for extortion in relation to computer fraud, and a mandatory two-year sentence consecutive to any other prison time for aggravated identity theft.
The third alleged co-conspirator is John Erin Binns, 26, an elusive American man who fled the United States after being indicted for his admitted role in a 2021 breach at T-Mobile that exposed the personal information of at least 76 million customers.
Sources close to the investigation said Binns, also known as “IRDev” and “IntelSecrets,” was until recently incarcerated in a Turkish prison, but that he has since been released and has resurfaced online. Those sources said Binns also recently obtained Turkish citizenship, and under Turkish law a citizen cannot be extradited to a foreign country.
An image of a passport that Binns shared in an email to KrebsOnSecurity in Feb. 2023.
Moucka pleaded guilty to four criminal counts, including computer fraud, wire fraud, aggravated identity theft, and conspiracy. He is slated to be sentenced on Oct. 27 and faces a mandatory minimum penalty of two years in prison on the aggravated identity theft count, as well as a maximum penalty of 30 years in prison on the remaining counts. Ultimately, it will be up the federal judge how much time Moucka actually serves for his extensive cybercriminal rap sheet.
Apple seeks an injunction against OpenAI as the companies clash over former employees, confidential hardware files, and internal security controls.
The post Apple Seeks Injunction as OpenAI Blames Tech Giant for Security Lapses appeared first on TechRepublic.
In 2025, the enterprise risk landscape experienced a paradigm shift: the adoption of AI and LLMs officially becoming the primary driver of cloud risk. Today, almost 88% of organizations now leverage AI in at least one business function. With this level of integration, the risk of AI is now outpacing traditional security guardrails, culminating in a highly complex and interconnected attack surface.
SentinelOne’s® new AI and Cloud Verified Exploit Paths and Secrets Scanning Report examines this ev
In 2025, the enterprise risk landscape experienced a paradigm shift: the adoption of AI and LLMs officially becoming the primary driver of cloud risk. Today, almost 88% of organizations now leverage AI in at least one business function. With this level of integration, the risk of AI is now outpacing traditional security guardrails, culminating in a highly complex and interconnected attack surface.
SentinelOne’s® new AI and Cloud Verified Exploit Paths and Secrets Scanning Report examines this evolving threatscape and draws on telemetry from over 11,000 anonymized customer environments to offer deeper visibility into how threat actors are actively exploiting modern cloud and AI infrastructures.
An Explosion of AI-Specific Secrets and Shadow AI
A primary finding of the 2026 report is the rising proliferation of AI-specific credentials. The data indicates that AI-related secrets — such as OpenAI API Keys, Azure OpenAI API Keys, and others — increased by approximately 140% in a span of one year. This growth correlates directly with the rapid embedding of AI technologies into customer support systems, internal tooling, financial platforms, and product experiences.
Ubiquitous deployment has generated a widespread organizational pattern known as “shadow AI” – the unsanctioned use of AI tools in an environment without formal IT approval or security oversight. In practice, this occurs when developers or internal teams utilize unmanaged or personal LLM keys to process corporate data outside of sanctioned IT or security channels. Since these AI integrations span numerous internal applications, the same API keys are frequently duplicated and stored within code repositories, SaaS configurations, and development scripts. Compounding this, these credentials are often implemented without proper access controls or routine rotation schedules.
The sprawl of these credentials renders them difficult to track via standard secrets management protocols, establishing a requirement for more centralized governance over how AI keys are issued and utilized.
Distinct Risk Vectors of Unmanaged AI Credentials
Unlike traditional cloud credentials that primarily facilitate resource manipulation, the compromise of AI keys introduces unique risk vectors. AI services frequently operate at the intersection of various enterprise systems, including CRM platforms, ticketing systems, and analytics tools, which means a single compromised LLM API key can provide an attacker with broad visibility into diverse datasets. The risks associated are categorized with exposed AI keys into two primary areas:
Data exposure and leakage: Unauthorized access via AI keys can expose sensitive or proprietary datasets processed by the models, embedded business logic, and internal user prompts and outputs. This enables attackers to harvest sensitive corporate conversations at scale.
Prompt injection and data poisoning: Unmanaged AI keys allow threat actors to actively manipulate AI models. Through prompt injection, an attacker can influence model behavior to exfiltrate data or bypass established security controls. Additionally, attackers can execute data poisoning by injecting misleading or malicious data into contextual corpora or fine-tuning datasets, which degrades the model’s integrity and reliability over time.
The Broadening Scope of Traditional Cloud Secrets
While AI credentials represent a novel attack surface, the traditional cloud secrets landscape has concurrently grown more complex. In 2025, organizations exposed approximately twice as many types of critical secrets as they did in 2024. This diversification spans AI platforms, cloud providers, SaaS services, and payment processors, pointing to how a single compromise can result in a broader blast radius across revenue-generating systems and infrastructure.
High-privilege cloud provider keys associated with AWS, Azure, and GCP remain the primary anchor of critical risk. The exposure of these keys can facilitate complete account takeover, infrastructure manipulation, and large-scale data exfiltration. As well, the exposure of payment gateway keys, such as those for Stripe and Razorpay, expands the potential damage by putting Personally Identifiable Information (PII) and financial data at risk, enabling the direct abuse of payment workflows.
Repository and CI/CD tokens also introduce supply chain risks, where high-severity credentials like a GITHUB_TOKEN can grant attackers direct access to deployment pipelines and source code, allowing a localized leak to escalate into a systemic infrastructure incident. From a collective standpoint, secrets exposure is exponentially spanning payments, coding, and software development workflows, making risk an interconnected and complex challenge.
Verified Exploit Paths: The Persistence of Legacy Vulnerabilities
To evaluate how these exposed secrets translate into practical risks, the SentinelOne researchers leveraged the Offensive Security Engine (OSE) to generate Verified Exploit Paths. This technology analyzes misconfigurations, vulnerabilities, and exposed secrets in context to determine realistic exploitability.
The telemetry demonstrates that attackers generally do not rely on highly complex, theoretical attack chains. Instead, threat actors consistently exploit recurring entry points, specifically targeting misconfigured external services and widely abused Common Vulnerabilities and Exposures (CVEs). Notably, legacy vulnerabilities remain highly prevalent across customer environments and serve as reliable initial access points. The top verified exploit paths continue to involve older, critical CVEs, including:
Since these vulnerabilities are public and well-documented, threat actors possess proven techniques and automated tooling to exploit them whenever they persist in production environments. Once initial access is achieved through these legacy vulnerabilities, attackers routinely follow reachable secrets to pivot into additional services, such as utilizing an exposed key found in a cloud bucket to access an AI assistant, and subsequently, the customer data it processes.
Strategic Recommendations for Security Leaders
Addressing the interconnected risks of AI integration and cloud secrets requires a structured, objective approach to security architecture. The report outlines several concrete capabilities and practices including:
Continuous Surface Monitoring: Organizations must regularly inventory internet-facing assets, databases, and key cloud services, ensuring any configuration changes are immediately reflected in security posture assessments.
DevSecOps Automation: Security controls must be embedded directly into CI/CD pipelines and developer workflows. Organizations should automate the scanning of exposed secrets and trigger safe remediation actions, such as access revocation or key rotation.
Governance of AI Credentials: AI keys must be classified and treated as high-value credentials. Organizations should mandate the use of centrally managed AI keys rather than personal credentials, enforce least-privilege access, implement regular rotation schedules, and continuously monitor for shadow AI usage or abnormal access patterns.
Conclusion
As AI systems are increasingly built atop existing cloud, payment, and CI/CD platforms, weaknesses in traditional credentials inevitably become weaknesses in the AI infrastructures that rely upon them. The full report provides complete datasets and comprehensive exploit path models allowing today’s security teams to align their internal security policies with the realities of current threat actor behaviors. Learn more about the objective metrics behind the latest wave of credential exposure and vulnerability exploitation to establish more resilient and fully-controlled infrastructure architectures.
Third-Party Trademark Disclaimer:
All third-party product names, logos, and brands mentioned in this publication are the property of their respective owners and are for identification purposes only. Use of these names, logos, and brands does not imply affiliation, endorsement, sponsorship, or association with the third-party.
Expose the AI & Cloud Secrets That Put Your Data & Systems at Risk
This report draws on 11K+ customer environments. It shows how AI and cloud adoption are increasing secrets exposure and putting data at risk.
5 min readA developer needs to connect a service to an API. The documentation says to generate an API key, store it in an environment variable and pass it in a header. Five minutes later, the integration works.
The post API Keys vs. JWTs: Choosing the Right Auth Method for Your API appeared first on Aembit.
The post API Keys vs. JWTs: Choosing the Right Auth Method for Your API appeared first on Security Boulevard.
5min readA developer needs to connect a service to an API. The documentation says to generate an API key, store it in an environment variable and pass it in a header. Five minutes later, the integration works.
6 min readMost organizations still treat credentials as something that must be protected, stored, and rotated. But a second model is quietly reshaping how machine authentication works: eliminate static secrets altogether and authenticate workloads using identity and just-in-time access.
The post Secrets Management vs. Secrets Elimination: Where Should You Invest? appeared first on Aembit.
The post Secrets Management vs. Secrets Elimination: Where Should You Invest? appeared first on Security B
6min readMost organizations still treat credentials as something that must be protected, stored, and rotated. But a second model is quietly reshaping how machine authentication works: eliminate static secrets altogether and authenticate workloads using identity and just-in-time access.
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