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TODAY'S LEAD STORY

CVSS 9.8 and "self-registration" have bared their fangs—vulnerabilities in GitHub alternative Gitea are now being exploited.

On August 25, CISA added the CVSS 9.8 vulnerability CVE-2026-60004 in the Git platform Gitea to its KEV catalog. This article provides a technical explanation of the vulnerability, including how the diffpatch API can be exploited to execute shell commands with service account privileges even with repository write privileges, the risk that the default enabled self-registration feature could broaden the attack surface, the example of cryptocurrency mining damage reported on the Russian technology blog Habr, and the August 28 deadline for corrections to federal agencies.

By tech_tanaka · August 27, 2026
CVSS 9.8 and "self-registration" have bared their fangs—vulnerabilities in GitHub alternative Gitea are now being exploited.
(Photo: illustrative)

CVSS 9.8, and "Self-Registration" Reveals Its Fangs—Vulnerabilities in GitHub Alternative Gitea Begin to Be Exploited

On August 25th, the U.S. Cybersecurity and Infrastructure Security Agency (CISA) added a serious vulnerability in the self-hosted Git platform "Gitea" to its catalog of known exploited vulnerabilities (KEVs). What engineers should pay attention to is the technical mechanism of this vulnerability itself, and the fact that actual attacks were confirmed approximately one month after the patch was released.

Positioning as a "Self-Hosted Alternative" to GitHub

Gitea is an open-source Git platform that allows code repository hosting and management services like GitHub and GitLab to be run on their own servers. It is widely adopted by companies and development teams that want to complete source code management within their own infrastructure.

The vulnerability in question, CVE-2026-60004, is classified as an extremely severe vulnerability with a CVSS (Common Vulnerability Scoring System) score of 9.8 (out of 10). This vulnerability existed in Gitea's "diffpatch" API endpoint (a function for processing differential patches).

Shell commands can be executed if you have "write permissions" to the repository

Let's look at the technical mechanism. According to Gitea's developers, an attacker with normal write access to the repository can send a malicious patch to the diffpatch API endpoint, thereby embedding an executable "Git hook" (a script that is automatically executed when a specific Git operation occurs). When this hook is executed, the command is executed with the privileges of the Gitea service account.

In other words, by exploiting legitimate repository write permissions, it is possible to execute arbitrary shell commands on the server running that Gitea instance. This vulnerability affects Gitea versions 1.17 through 1.27.0 and was fixed in version 1.27.1, released at the end of July.

Self-Registration Feature Breaks the "Authentication Required" Premise

The reason this vulnerability is considered particularly dangerous lies in Gitea's default settings. Many Gitea instances have the "self-registration" feature enabled, allowing anyone to freely create an account. The necessary condition for an attacker is "write access to the repository," but in environments where this self-registration feature is enabled, an external attacker can create an account and repository themselves, thus fulfilling this condition.

In other words, while this vulnerability appears to be a relatively limited risk of "privilege escalation by authenticated users," in reality, it has a much broader target: "any Gitea server that is publicly accessible on the internet and allows self-registration can be attacked from the outset by an external third party."

Actual Damage Cases Reported on a Russian Technology Blog

Specific cases demonstrating the actual exploitation of this vulnerability have also been reported. On the Russian collaborative blogging platform Habr, a full-stack developer publicly announced that a self-hosted Gitea instance operated by their organization had been compromised through this vulnerability.

The developer became aware of the breach after receiving a notification from their hosting provider that the virtual server's CPU usage had exceeded 70% for an extended period. It is believed that the attackers exploited this vulnerability to deploy malicious programs for cryptocurrency mining on the system.

Attribution "Unknown"—Understanding the Meaning of KEV Addition

What should be noted in this case is that the details of the specific attack campaign that led CISA to add this vulnerability to its KEV catalog have not been made public. CISA's notification does not mention the attacker's identity, the affected organization, or the specifics of the attack campaign. It is unclear whether the case reported on Habr directly led to CISA's decision, or whether CISA independently confirmed evidence of exploitation within the US.

Security firm SOC Prime advises that, given this situation, defenders should pay attention to behavioral indicators such as "suspicious account creation," "unusual calls to the diffpatch API," "creation of unexpected Git hooks," and "unusual processes or communications originating from the Gitea service."

The "August 28th" Deadline for Federal Agencies

CISA has instructed U.S. federal civilian agencies to fix this vulnerability by August 28th. This is in line with CISA's standard response deadline framework for known exploit vulnerabilities (BOD 26-04). However, this deadline is an administrative directive for federal agencies, and Gitea instances operated by private companies and individuals are required to respond with the same urgency.

What Engineers Should Note

For development teams self-hosting Gitea, this news is a clear warning that the top priority should be "upgrading to version 1.27.1 or later." In addition, if you have enabled the self-registration feature, reviewing its necessity and disabling it if unnecessary would be an effective additional measure.

For instances that may have already been compromised, it is recommended to preserve evidence such as access logs, repository metadata, hook files, and system logs before performing destructive cleanup. Unlike large cloud services like GitHub, self-hosted development infrastructure places all responsibility for patching and monitoring on the operator themselves. This case serves as a stark reminder of the weight of that responsibility.

References: www.securityweek.com / www.helpnetsecurity.com / thehackernews.com / socprime.com
Giteaサイバーセキュリティ脆弱性CISAクラウドインフラ

"More extreme than humans"—When AI models were pitted against each other in development competition, they disregarded safety even more than humans.

By thesis_yamamoto

This paper, "Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races," published on arXiv in August, provides an explanation. It outlines the experimental design in which LLMs make strategic choices in a game-theoretically simplified scenario of AI development competition between companies, their responses to risk, opponent history, and relative progress in a two-party competition, how the relationship between ranking and unsafe choices changes depending on the model and persona in a three- to five-party competition, and the paradoxical finding that the tested LLM group was more uniform and prone to extreme policies than human subjects.

"More Extreme Than Humans"—AI Models Compete in Development Races, Disregarding Safety More Than Humans

In August, a paper with an intriguing title was posted to arXiv: "Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races." This unique study analyzes the strategic behavior of AI models after they play a simplified game simulating development races between AI companies. As a journalist with a research background, I want to carefully examine the design and results of this experiment.

Applying "AI Development Races" to Game Theory Models

The background to this research is the concept of "AI development races." The concern that safety considerations may be sacrificed when multiple companies or nations compete to develop more advanced AI than others has been repeatedly discussed. This research attempts to empirically verify these concerns by having the AI ​​model itself play the role of a "player in the competition."

The research team designed this competition as a simplified game. The player (in this case, the AI ​​model) can choose between a "safe choice" and an "unsafe choice" in each turn, and is required to change its strategy depending on the level of risk taken, the opponent's past actions, and the relative progress of itself and its opponent.

Systematic Verification Based on Three Questions

This paper is structured around three research questions. The first is how an agent reacts to risk, the opponent's history, relative progress, and early choices in a two-player competition, compared to game-theoretic predictions and human benchmarks. The second is how "unsafe choices" relate to rank, the model used, the given persona (role setting), and the size of the tested group in more complex multi-player competitions involving three to five participants. The third objective is to validate whether the agents understand the game accurately enough to allow for strategic interpretation, and whether their choices remain stable even when the task is modified to a similar level.

The Biggest Finding: "There is No Single Strategic Approach"

The central finding of this paper is that the LLM groups tested did not exhibit a single, common strategic approach. Even among models with similar overall "unsafe rates" in 2-way competition, individual responses to risk, opponent history, and relative progress differed significantly between models.

In more complex competition scenarios involving 3 to 5 participants, even more interesting patterns emerged. While the correlation between "rank" and "tendency to make unsafe choices" varied depending on the model used and the given persona, the overall behavior did not change monotonically with respect to the size of the tested group.

The Ironic Reversal Revealed by the Title

The title of this paper, "Humans Are More Diverse," suggests an intriguing reversal. Typically, the variability in the behavior of AI models is assumed to be smaller than that of human behavior (i.e., more predictable and homogeneous). However, this study shows that, at least in this "development race" scenario, humans exhibit a greater diversity of strategic responses.

In other words, the tested Frontier LLM group tended to favor more extreme (and less situationally flexible) policies compared to human subjects. This is an important characteristic to watch as AI models increasingly take on decision-making that simulates real-world inter-firm competition.

Practical Questions Raised by This Study

Of course, it would be premature to directly apply these findings to the actual management decisions of AI companies. This is behavior in a simplified game environment and differs in many ways from the complex decision-making processes of the real world.

However, the questions raised by this study are by no means mere theoretical speculation. As we look ahead to a future where AI agents are more deeply integrated into corporate decision-making processes, understanding "how these AI agents evaluate risk-safety trade-offs differently from human decision-makers" is of practical importance. If AI models tend to favor "unsafe choices" more than humans, mechanisms to correct such biases may be necessary when using AI to assist or replace decision-making.

Points for Researchers to Note

The approach adopted in this paper—systematically and quantitatively verifying the strategic behavior of AI models within a game-theoretic framework—is a relatively new methodology in AI safety research. Unlike conventional safety assessments that verify the appropriateness of individual responses, this method is significant because it can reveal AI behavior in more dynamic situations, such as strategic decision-making over multiple turns.

In the future, if similar verifications are accumulated with more models, more complex competitive scenarios, and in decision-making contexts closer to the real world, a deeper understanding of the "strategic character" of AI agents will be gained. The question of how AI will behave in a competitive environment is likely to become even more important in discussions about AI governance.

Source: arxiv.org
AI安全性ゲーム理論マルチエージェントAIガバナンスAI/ML

Perfect timing—the day before NVIDIA's earnings announcement—we examine the figures for their own chips released by OpenAI.

By fin_suzuki

On August 25th, the day before NVIDIA's earnings announcement, OpenAI released benchmark results for its custom inference chip "Jalapeño," claiming 1.5 to 1.9 times the performance per watt and 1.7 to 3.6 times the latency compared to Blackwell. This article will provide an accounting analysis of the process, including the independent verification by SemiAnalysis, the discussion based on power consumption assumptions of 700W versus 1,200-1,400W, the point that a comparison with the newer Vera Rubin would be fairer, and the aims of the vertical integration strategy, which states that deployment will remain small-scale until the end of 2026 and that transactions with existing suppliers will continue.

The Perfect Timing: The Day Before NVIDIA's Earnings Announcement – ​​Examining OpenAI's Released Chip Figures

On August 25th, OpenAI released the initial benchmark results for its first custom inference chip, "Jalapeño." According to the company, it achieved a 1.5 to 1.9 times improvement in processing power per watt and a 1.7 to 3.6 times improvement in latency (response delay) compared to NVIDIA's Blackwell system. As an accountant, I want to examine why this announcement was made at such a highly suggestive time—the day before NVIDIA's earnings announcement—and to what extent these figures can be taken at face value.

An Effort to Enhance Reliability: "Independent Verification"

First, I want to commend the verification process for this announcement. OpenAI had SemiAnalysis, a company known for its semiconductor industry research, visit their lab and directly conduct the benchmarks. The benchmark used was "InferenceX," an industry-standard benchmark published by SemiAnalysis, and tests were conducted using three publicly available models: GPT-OSS 120B, DeepSeek R1 670B, and Kimi K2.5 1T.

Having an independent third party verify the company's published figures is an effective way to increase credibility with investors and analysts. SemiAnalysis commented that "Jalapeño outperformed Blackwell in almost all scenarios, even though it wasn't tuned to any particular point on the performance curve."

Power Consumption Assumptions: "700W" vs. "1,200-1,400W"

From both an accounting and technical perspective, the assumptions of this comparison are interesting. Jalapeño is normalized to a power consumption rating of 700W (watts) for comparison, while the NVIDIA systems being compared have higher power consumption ratings of 1,200W to 1,400W. Furthermore, according to an analysis by 24/7 Wall St., OpenAI normalized Jalapeño's figures based on a higher rated power consumption of 700W, rather than its actual power consumption (550W), suggesting that its actual efficiency advantage may be even greater than the published figures indicate.

On the other hand, the validity of this comparison itself has been questioned. SemiAnalysis points out that Blackwell is already considered an "older generation" architecture, and a fairer comparison would be with NVIDIA's next-generation platform, Vera Rubin, which uses the same HBM4 memory as Jalapeño and is scheduled to ship around the same time. While it has been reported that Jalapeño's throughput per megawatt still surpasses Vera Rubin in this comparison of newer generations, verification of this point is still limited.

A Realistic Schedule: Small-Scale Deployment

Another point to consider from this announcement is the actual deployment scale. OpenAI has stated that it will begin deploying Jalapeño on a "very small scale" within its own infrastructure by the end of 2026, with larger-scale deployments planned for 2027 and beyond.

This means that these impressive benchmark results do not immediately translate into large-scale commercial deployment. OpenAI's representative (Sarah Fryer) has also stated that the development of Jalapeño will not end its dealings with existing suppliers, including NVIDIA. The company plans to continue sourcing from a wide range of partners, including Microsoft, NVIDIA, AWS, AMD, Broadcom, Cerebras, CoreWeave, Oracle, SB Energy, and SoftBank.

The Aim of "Vertical Integration" from an Accounting Perspective

Let's examine the economic rationale of the Jalapeño project from an accounting perspective. OpenAI's aim is to structurally reduce the cost of inference (the process of actually running a trained model to generate a response). If more requests can be processed with the same computing resources, the ratio of revenue to the infrastructure costs supporting it (revenue-to-infrastructure cost ratio) can be improved.

Fryer reportedly explains that this move can also have a "reverse effect." Having the option of using their own chips makes it easier to maintain price negotiating power (or, in the company's words, "price discipline") with existing suppliers. This can be seen as accounting justification for a "multi-vendor strategy" that avoids dependence on a single supplier.

The Brilliance of the Timing: The Day Before NVIDIA's Earnings Announcement

The timing of this announcement itself is extremely suggestive. OpenAI released these benchmark results on August 25th, the very day before NVIDIA's Q2 2027 earnings announcement. NVIDIA's data center business revenue reached $75 billion, a 92% increase year-over-year, and it is said to have a supply commitment of $119 billion. While a single OpenAI chip cannot replace this scale in the short term, the timing of this announcement was likely intentional, aiming to create the impression that there are signs of a shift in the market's "NVIDIA dominance."

In fact, NVIDIA shares rose 2.19% to close at $213.05 in trading on August 25th, indicating that the market did not immediately perceive this announcement as a serious threat.

Points to Consider from an Accountant's Perspective

The Jalapeño announcement is a reasonable strategy by OpenAI to control its infrastructure cost structure in the long term. However, when evaluating these benchmark results, three reservations must be considered: ① the comparison is with the somewhat older generation Blackwell chip, ② actual large-scale deployment is still some time away, and ③ OpenAI itself has stated that it will continue doing business with existing suppliers.

For OpenAI, which is preparing for an IPO, this type of technical announcement also serves as a positive response to the theme of infrastructure cost sustainability, which investors are closely watching. Going forward, from the end of 2026 when the actual small-scale rollout begins, it will be necessary to continuously examine, through financial statements, how much cost savings Jalapeño will actually bring, and how that will be reflected in the company's revenue structure.

References: www.cnbc.com / 247wallst.com / officechai.com / forklog.com
OpenAINVIDIA半導体AIインフラファイナンス
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