Loading story…
OpenAI has made automated security review mandatory for every engineer pull request, authorizing AI models to block merges when they detect vulnerabilities. Tibo Sottiaux, who leads OpenAI’s Core Products & Platform organization and Codex work, said specialized review capabilities have been integrated into mainline models and benchmarked as superhuman for correctness and security-focused code review.
OpenAI is also using Codex-driven automation for regression detection, deployments, dependency upgrades, routine patching, and larger refactoring work. Codex, implemented in Rust with a harness that provides models safety controls, efficiency, steerability, and context, is used across the company’s code, documents, and Slack; Sottiaux expects engineers to focus more on specifying and validating intended changes. Automated review remains exposed to false positives, missed flaws, shared blind spots between code-generation and review models, and supply-chain risks that pull-request analysis may not detect.
Track how attackers are adapting to this technology.
1 event from the most recent confirmed update back to the earliest known activity.
OpenAI requires every engineer pull request to undergo automated security review. Its AI reviewer can block merges without human-reviewer enforcement when it identifies a security vulnerability.
Vulnerabilities, threat actors, malware, products, organizations, and breaches Mallory has linked to this story.
Follow how adversaries are adapting to this technology, and where it touches your stack today.
2 references tracked. Mallory keeps watching after this page renders.
Map indicators from this story to your assets and identify affected systems in minutes.
Every observed campaign, victim, and pivot linked to actors named in this story.
Malware, exploits, and IOCs connected to the activity described here.
YARA, Sigma, and Snort rules deployed to your SIEM as soon as they’re published.
Get matching new stories delivered to your team as they break — not the next morning.
Ask questions about this story and take action on the answers.
Other stories Mallory is tracking that overlap on entities, sources, or topic labels.
OpenAI has released GPT-5.2-Codex, a new AI model designed to enhance agentic coding and cybersecurity tasks, with notable improvements in vulnerability detection and software engineering workflows. The model demonstrates superior performance on benchmarks such as SWE-Bench Pro and Terminal-Bench 2.0, and excels in professional Capture-the-Flag challenges, supporting advanced tasks like fuzzing, attack surface analysis, and test environment setup. OpenAI has implemented stronger safeguards to mitigate dual-use risks and is offering the model to paid ChatGPT Codex users, with an invite-only pilot for vetted cybersecurity professionals focused on defensive applications. The model's capabilities have already contributed to the discovery of several critical vulnerabilities in React Server Components, including CVE-2025-55182 (RCE), CVE-2025-55183 (source code exposure), and CVE-2025-67779 (DoS), prompting urgent patching recommendations. Industry research highlights both the promise and risks of AI-generated code, with studies showing that machine-written pull requests contain significantly more bugs and security vulnerabilities than those authored solely by humans. Issues such as improper password handling, insecure object references, and cross-site scripting are notably more prevalent in AI-generated code, raising concerns about the security implications of rapid AI-driven development. These findings underscore the importance of robust review processes and the need for continued vigilance as AI tools become more deeply integrated into software engineering and cybersecurity operations.
Jun 29, 2026
Enterprises are accelerating adoption of AI coding assistants and agents, with vendors and engineering leaders reorganizing around tools such as GitHub Copilot, Codex, Claude Code, and formal-verification agents to increase software output and support smaller teams. OpenAI has reportedly shifted strategy toward coding and enterprise customers, while companies such as Cursor are deploying always-on security agents that scan pull requests, patch dependencies, and block risky changes before release. Research and industry reporting also show AI is changing how developers work: Copilot users spend more time coding and less time on collaboration and project-management tasks, and engineering teams increasingly treat AI as a force multiplier rather than a replacement for human developers. At the same time, multiple studies and incident reports say the gains are being offset by a growing **review and supervision tax**. Reports from Harness, DORA, Sonar, and other researchers found that heavier AI use often correlates with more code review effort, higher defect and deployment risk, longer remediation and recovery times, and rising burnout among senior engineers who must validate AI-generated changes. Security researchers at RSAC 2026 said AI assistants reproduce common flaws such as `SSRF`, `XSS`, path traversal, command injection, and open redirects at scale, while reporting tied AI-assisted changes to outages and operational failures, including an AWS disruption. Across the coverage, experts urged organizations to keep humans in the loop, apply stricter governance and testing for AI-generated code, and measure validation workload, defect escape, and technical debt rather than code volume alone.
Jul 1, 2026
Multiple studies and industry reports found that **AI-generated or "vibe-coded" software is frequently shipped with serious security flaws**, including XSS, SQL injection, hardcoded secrets, unsafe `eval` usage, weak authentication logic, wildcard CORS, exposed `.env` data, and insecure dependencies. One analysis of 5,600 publicly accessible AI-built applications reported more than 2,000 high-risk vulnerabilities and over 400 exposed secrets, while a separate review of 549 self-described AI-generated repositories found widespread committed secrets, injection issues, hardcoded passwords, and other common sanitization failures. Veracode and other researchers similarly warned that code produced by large language models often maps to **OWASP Top 10** weakness classes and that repeated AI-assisted edits, poor prompts, and excessive agent permissions can further increase risk. At the same time, researchers and vendors highlighted both the promise and fragility of AI-assisted security tooling. Trail of Bits reported that OpenAI Codex goal-based prompting helped uncover previously unknown bugs in projects including **Rust**, **curl**, **zlib**, and **Keycloak**, but stressed that human experts were still needed to scope goals, validate findings, and handle disclosure. New academic work also showed that **LLM-based vulnerability detectors can be easily misled**: the ALIBI framework used adversarial code comments to evade four detectors with success rates above 90%. In response to growing concern over agent reliability, Amazon backed the Lean language project to strengthen formal verification for AI agents, while security experts argued that theorem proving can improve assurance for high-risk components but should be applied selectively as part of broader defense-in-depth.
Aug 4, 2026