Fujitsu is a global leader in digital services that transform organizations and the world around us. We aim to make the world more sustainable by building trust in society through innovation.
Appen has been a leader in AI training data for over 25 years, providing high-quality, diverse datasets that power the world's leading AI models. Our end-to-end platform, deep expertise, and scalable human-in-the-loop services enable AI innovators to build and optimize cutting-edge models. We specialize in creating bespoke, human-generated data to train, fine-tune, and evaluate AI models across multiple domains, including generative AI, large language models (LLMs), computer vision, speech recognition, and more. Our solutions support critical AI functions such as supervised fine-tuning, reinforcement learning with human feedback (RLHF), model evaluation, and bias mitigation. Our advanced AI-assisted data annotation platform, combined with a global crowd of more than 1M contributors in over 200 countries, ensures the delivery of accurate and diverse datasets. Our commitment to quality, scalability, and ethical AI practices makes Appen a trusted partner for enterprises aiming to develop and deploy effective AI solutions. At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest-quality AI solutions. We support work-life balance with flexible work arrangements and a dynamic, results-driven environment. Employees have access to competitive pay, comprehensive benefits, and opportunities for continuous learning and career growth. Our team works closely with the world’s top technology companies and enterprises, tackling exciting challenges and shaping the future of artificial intelligence.
Security & Compliance Standards Overview
No incidents recorded for Fujitsu in 2025.
No incidents recorded for Appen in 2025.
Fujitsu cyber incidents detection timeline including parent company and subsidiaries
Appen cyber incidents detection timeline including parent company and subsidiaries
Last 3 Security & Risk Events by Company
Angular is a development platform for building mobile and desktop web applications using TypeScript/JavaScript and other languages. Prior to versions 19.2.16, 20.3.14, and 21.0.1, there is a XSRF token leakage via protocol-relative URLs in angular HTTP clients. The vulnerability is a Credential Leak by App Logic that leads to the unauthorized disclosure of the Cross-Site Request Forgery (XSRF) token to an attacker-controlled domain. Angular's HttpClient has a built-in XSRF protection mechanism that works by checking if a request URL starts with a protocol (http:// or https://) to determine if it is cross-origin. If the URL starts with protocol-relative URL (//), it is incorrectly treated as a same-origin request, and the XSRF token is automatically added to the X-XSRF-TOKEN header. This issue has been patched in versions 19.2.16, 20.3.14, and 21.0.1. A workaround for this issue involves avoiding using protocol-relative URLs (URLs starting with //) in HttpClient requests. All backend communication URLs should be hardcoded as relative paths (starting with a single /) or fully qualified, trusted absolute URLs.
Forge (also called `node-forge`) is a native implementation of Transport Layer Security in JavaScript. An Uncontrolled Recursion vulnerability in node-forge versions 1.3.1 and below enables remote, unauthenticated attackers to craft deep ASN.1 structures that trigger unbounded recursive parsing. This leads to a Denial-of-Service (DoS) via stack exhaustion when parsing untrusted DER inputs. This issue has been patched in version 1.3.2.
Forge (also called `node-forge`) is a native implementation of Transport Layer Security in JavaScript. An Integer Overflow vulnerability in node-forge versions 1.3.1 and below enables remote, unauthenticated attackers to craft ASN.1 structures containing OIDs with oversized arcs. These arcs may be decoded as smaller, trusted OIDs due to 32-bit bitwise truncation, enabling the bypass of downstream OID-based security decisions. This issue has been patched in version 1.3.2.
Suricata is a network IDS, IPS and NSM engine developed by the OISF (Open Information Security Foundation) and the Suricata community. Prior to versions 7.0.13 and 8.0.2, working with large buffers in Lua scripts can lead to a stack overflow. Users of Lua rules and output scripts may be affected when working with large buffers. This includes a rule passing a large buffer to a Lua script. This issue has been patched in versions 7.0.13 and 8.0.2. A workaround for this issue involves disabling Lua rules and output scripts, or making sure limits, such as stream.depth.reassembly and HTTP response body limits (response-body-limit), are set to less than half the stack size.
Suricata is a network IDS, IPS and NSM engine developed by the OISF (Open Information Security Foundation) and the Suricata community. In versions from 8.0.0 to before 8.0.2, a NULL dereference can occur when the entropy keyword is used in conjunction with base64_data. This issue has been patched in version 8.0.2. A workaround involves disabling rules that use entropy in conjunction with base64_data.
