OpenAI on Monday unveiled a new cybersecurity-focused model called GPT‑5.6‑Cyber that it said is focused on vulnerability research, penetration testing, and incident response.
“Built on GPT‑5.6 Sol, it is trained to improve capabilities on several specialized cybersecurity tasks (e.g., finding zero-day vulnerabilities and developing exploit chains) and to reduce refusals for certain higher-risk, dual-use cyber tasks,” OpenAI said.
The artificial intelligence (AI) company said it’s making GPT 5.6 Cyber available through Daybreak Red, a new tier that provides access to its purpose-trained cybersecurity models to other firms for authorized vulnerability research, exploit validation, and security testing.
GPT-5.6-Cyber, a more cyber-permissive version of GPT-5.6 Sol, builds upon GPT‑5.5‑Cyber, which OpenAI released in June 2026.
To measure the reduced rate of refusals provided by GPT‑5.6‑Cyber through Daybreak Red access, OpenAI said it created an internal evaluation called Advanced Cybersecurity Completion Rate that measures how often models respond to prompts related to exploit-chain development, authentication bypass, privilege escalation, and other advanced cybersecurity scenarios.
The tests show that GPT‑5.6‑Cyber completes 95.0% of these requests, compared with just 1.5% for GPT‑5.6 Sol and 2.0% when used with Daybreak Blue access. It has also been found to successfully complete more requests than GPT‑5.5‑Cyber, which finished only 57.3% of requests.
GPT‑5.6‑Cyber is trained to improve performance on certain cybersecurity workflows involving exploit development and advanced security research. An ExploitGym benchmark evaluation has revealed the model to outperform both GPT‑5.6 Sol and GPT‑5.5 Cyber.
OpenAI said the model also demonstrates improvements when it comes to finding and accurately calibrating the severity of novel zero-day vulnerabilities due to specialized training, although it performs worse than GPT‑5.6 Sol when it comes to open-ended quests associated with uncovering vulnerabilities in a repository, developing a working proof-of-concept, and submitting a high-quality vulnerability report.
This, the company noted, is due to “the model sometimes producing shorter, less detailed vulnerability reports.”
One of the high-severity vulnerabilities discovered by the model is CVE-2026-15903 (CVSS score: 8.8), an out-of-bounds read and write vulnerability in the V8 JavaScript engine that could allow a remote attacker to potentially execute arbitrary code inside a sandbox via a crafted HTML page.
It could be chained with another previously unknown vulnerability, also found by the model, to escape the V8 heap sandbox. CVE-2026-15903 was patched by Google in mid-July 2026. OpenAI said the model has also been used to flag several other flaws –
- At least five vulnerabilities in a popular mobile operating system, including a chain from an untrusted app to local privilege escalation
- Three critical vulnerabilities in a popular database, including a remote path to code execution
- Over 400 vulnerabilities that can lead to privilege escalation in a popular operating system kernel
Daybreak Red is one of two access tiers set up by OpenAI as part of the Daybreak initiative it introduced back in May 2026, the other being Daybreak Blue, which provides access to frontier general-purpose models, including GPT‑5.6 Sol, with built-in guardrails tailored to authorized defensive security work.
“Daybreak Blue access removes those guardrails, helping defenders get more out of the model in real-world security tasks, including incident detection and response, investigations, vulnerability management, and security assessments,” the company said.
GPT‑5.6‑Cyber has been made available to a group of trusted customer partners like Accenture, Akamai, Cisco, Cloudflare, CrowdStrike, Fortinet, IBM, Palo Alto Networks, PwC, and Sophos to help identify and patch vulnerabilities before attackers can exploit them and close the “defense gap.”
These models are being pitched to companies as a way to flag security vulnerabilities in software, as bad actors have significantly ramped up their use of the technology to enhance campaigns and carry out cyber attacks at speed and scale never seen before, even if it hasn’t led to the discovery of novel or sophisticated attack techniques.
What’s evident is that AI agents are enabling cybercriminals and nation-state hackers to outsource the grunt work needed to plan and carry out cyber attacks, offering them a way to improve the efficiency and productivity of their operations, resulting in attacks that are better, bigger, and faster.
To make matters worse, AI has also shortened the path from vulnerability disclosure to exploitation, with attackers leaning on such tools to write vibe exploits for newly disclosed flaws. With AI already lowering the barrier to exploit development and accelerating vulnerability research, attackers are likely to cast a wider net across disclosed vulnerabilities going forward to find a way into enterprise networks.
While AI systems have vastly improved at finding and exploiting vulnerabilities in software, they still require substantial human expertise, even as research has found that cyber-capable reasoning models like ChatGPT 5.5 and Anthropic Claude Opus 4.8 can struggle to fully patch a discovered vulnerability or avoid introducing new issues with their fixes.
“The average success rate for generating a patch that fully resolved the vulnerability (without materially changing application behavior) was just 26.0%,” 1Password said. “Patches that successfully resolved the vulnerability, but altered the application’s behavior in the process, occurred 20.1% of the time. Conversely, LLM-generated patches did not resolve the vulnerability, added a new vulnerability, or both, an average of 53.9% of the time.”
The findings underscore that models currently excelling at discovering a wide range of vulnerabilities are only good at effectively patching a “narrow subset” of them and help steer developers away from scenarios where the models either introduce new bugs regardless of whether an existing issue was patched or not, effectively expanding an attack surface for malicious actors to exploit.
“Models running with reduced safeguards carry risks beyond standard model usage, whether from misuse or misalignment,” OpenAI said. “Despite these risks, we believe that democratizing access to frontier intelligence for defenders is crucial to accelerating and automating cyber defense.”
