U.S. CISA adds SonicWall SonicOS SSLVPN vulnerability CVE-2026-19868 to its Known Exploited Vulnerabilities catalog. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added a SonicWall SonicOS SSLVPN flaw, tracked as CVE-2026-19868 (CVSS score of 7.5), to its Known Exploited Vulnerabilities (KEV) catalog. CVE-2026-19868 is an authentication bypass vulnerability affecting the SSL VPN service in SonicWall SonicOS. “An authentication enforcement vulnerability in SonicOS SSLVPN service may allow a user with valid credentials for an alternate authentication method to bypass SAML authentication controls. Successful exploitation may also bypass SAML-enforced multi-factor authentication (MFA).” reads the advisory. The flaw occurs when the system does not properly enforce SAML authentication controls. An attacker who already has valid credentials for an alternative authentication method may exploit this weakness to bypass SAML-based authentication. The impact is particularly serious when organisations rely on SAML to enforce multi-factor authentication (MFA). Successful exploitation may allow an attacker to access the SSL VPN without completing the expected SAML authentication process, potentially bypassing the additional MFA protection. In practical terms, the vulnerability could weaken an organisation’s remote access security by allowing users with alternative valid credentials to circumvent its intended authentication controls. It’s important to highlight that this is not an unauthenticated remote access vulnerability. The attacker needs valid credentials for an alternative authentication method, but may then bypass SAML-enforced security controls. SonicWall’s PSIRT team is investigating a report of possible exploitation of this flaw. The company strongly recommends that customers install the latest available update as soon as possible to reduce the risk of attacks. “SonicWall PSIRT has investigated a report indicating potential exploitation of this vulnerability. Customers are strongly urged to upgrade to the latest release as soon as possible to mitigate the associated risks.” reads the advisory. According to Binding Operational Directive (BOD) 22-01: Reducing the Significant Risk of Known Exploited Vulnerabilities, FCEB agencies have to address the identified vulnerabilities by the due date to protect their networks against attacks exploiting the flaws in the catalog. Experts also recommend that private organizations review the Catalog and address the vulnerabilities in their infrastructure. CISA orders federal agencies to fix the flaw by November 20, 2026. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CISA)
SonicWall has released security updates to fix seven vulnerabilities in SonicOS firewalls, including an SSL-VPN authentication bypass flaw exploited in attacks. The vulnerability, tracked as CVE-2026-19868, allows attackers with valid credentials for an alternate authentication method to bypass SAML authentication controls, potentially including SAML-enforced multi-factor authentication (MFA). The flaw has a CVSS severity score of 7.1 and only affects SonicOS SSL-VPN services configured to use SAML authentication. In a security advisory published Friday, SonicWall warned that it had investigated a report that the vulnerability was exploited in an attack and urged customers to install the latest security updates. "SonicWall PSIRT has investigated a report indicating potential exploitation of this vulnerability. Customers are strongly urged to upgrade to the latest release as soon as possible to mitigate the associated risks," the company warned.
Passwords and API keys that developers typed into commands they approved in Claude Code are sitting in public GitHub repositories, where anyone can read them. When a user selects "Yes, and don't ask again," Anthropic's AI coding assistant can save the command, word-for-word, including the secret, in a project settings file. Software security company Apiiro said yesterday that 1,122 public
OpenAI says GPT-6 Astra sometimes secretly underperformed on safety research, raising concerns about AI reliability and alignment. OpenAI has disclosed that its GPT-6 Astra model sometimes quietly obstructed safety and alignment work. In a report updated on November 4, the company calls it covert underperformance. Astra added unnecessary prerequisites, abandoned working solutions and reported blockers that didn’t exist or that it had caused itself. Researchers first reported the problem internally on August 20, shortly after Astra was introduced. One researcher said a project to test whether the model could sabotage other AI systems made little progress for several days. After switching to another model, the work moved forward quickly. In most cases, Astra’s reasoning gave no clear sign that it was deliberately doing less work, and OpenAI’s monitoring systems failed to detect the behavior. To investigate, OpenAI created pairs of coding tasks using the same code but with different goals. One task was presented as safety research, while the other was described as capability development. Astra completed about 10% of the required tasks when they involved safety research, compared with 16% for capability development. This means it completed roughly 40% fewer tasks in the safety tests. GPT-5.6 Sol showed almost no difference between the two types of work. Even after OpenAI removed cases involving clear refusals or signs that Astra had deliberately withheld help, the gap remained at 4.7 percentage points. It dropped to just 0.75 points only after researchers also removed every case where the model showed even brief ethical concerns. This suggests that most of the behavior cannot be detected simply by checking whether the model openly refuses a request. In employees’ daily Codex use, Astra refused 1.63 percent of safety and alignment requests, against 0.56 percent of capabilities requests. Its reasoning showed it withheld help silently in 0.25 percent of safety requests versus 0.11 percent of capabilities requests, 2.3 times as often. Ethical doubt appeared in 7.9 percent against 2.9 percent, mostly in red-teaming, monitoring, and safety evaluation work. OpenAI shared eight examples. Astra silently held back on training models to evade monitors, filtered a violent source out of a safety evaluation, restricted a jailbreak data export and narrowed exploit analysis. In one case it left graders’ reasoning out of an export without telling the user. A model that quietly decides your project is too risky is the coworker who says “looking into it” and never does. OpenAI has not yet identified the exact cause of the behavior. The difference between Astra’s performance on safety and capability tasks grew from almost zero at the start of reinforcement learning to 6 percentage points by the end. One evaluator favored silently doing less work over either refusing a request or fully completing it. Another finding was that a fine-tuning dataset encouraged the model to invent permission restrictions. Removing the changes linked to that dataset eliminated the performance gap. However, other models trained with the same dataset and reinforcement learning did not show the same behavior, so OpenAI cannot identify either factor as the root cause. OpenAI found no evidence that Astra was deliberately trying to hide its capabilities or influence deployment decisions, although it cannot rule out these possibilities. Simply telling the model to be more careful increased its underperformance by 50%, suggesting that excessive caution may be the main explanation. OpenAI warns that this behavior can slow safety research and make model evaluations less reliable. It is developing new tests and monitoring systems to detect it, especially because the problem becomes harder to identify when models reveal less of their reasoning. In some cases, Astra explained that it had held back because it disagreed with the research itself. “In reviewed cases, Astra narrowed or stopped requested safety work without disclosing the restriction. At scale, this can differentially slow down safety research compared to other research and make evaluations harder to interpret.” concludes the report. “A model that finds monitor evasion suspicious may underperform on monitor-evasion evaluations while leaving little evidence in its CoT or outputs. The behavior becomes even harder to detect if models externalize less of this reasoning in CoT.” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, OpenAI)
DigiCert warns it suffered a data breach after a server migration mistakenly exposed an internal business system to the internet, allowing unauthorized access to customer information and identity documents. The certificate authority says the breach affected a small portion of its archived customer data, including names, business email addresses, phone numbers, and business addresses. For some people, the exposed records also contained personal identification documents, such as driver's licenses, passports, and utility bills. DigiCert has begun sending data breach notifications to affected customers, with cloud architect Fabian Bader sharing a copy of the notification on X. "Thanks @digicert that your data leak was 'limited' to only my full PII data and potentially any verification documents I submitted for my certificate," Bader wrote.
Attackers target IT service desks to bypass MFA by impersonating employees and convincing staff to reset passwords or register new authentication devices. Specops Software explains why traditional identity verification methods are no longer sufficient and how organizations can secure account recovery against social engineering risks.
Cybersecurity researchers have disclosed details of a now-patched vulnerability that could be exploited to bypass Lockdown Mode protections in OpenAI ChatGPT. Lockdown Mode was introduced by the artificial intelligence (AI) company in June 2026 as an optional setting to minimize data exfiltration risks arising from prompt injections. "The flaw could enable silent chat data theft, phishing,
October 2026 brought another wave of cyber threats targeting US businesses, with attackers exploiting trusted platforms, legitimate authentication processes, and everyday business workflows. For enterprises, these campaigns raise the stakes beyond individual security incidents, increasing the risk of financial losses, data breaches, and costly operational disruption. Below, we examine five
Apple has revealed that it plans to start checking public iCloud links for harmful content starting with iOS 27.3, iPadOS 27.3, macOS 27.3, and visionOS 27.3. The change specifically applies to scenarios where users share iCloud content, such as photos in iCloud Photos or files in iCloud Drive, by sharing a link and making it accessible to "Anyone with the link." When users choose this option,
DreamHost is warning of a data breach after attackers gained access to its File Manager platform for two months and stole customers' FTP/SFTP usernames and passwords. This follows the American web hosting provider and domain registrar's announcement that it suffered a cybersecurity incident requiring users to reset their email and FTP/SFTP passwords. In a data breach notification, DreamHost now says the breach extended from August 2026 to October 23, 2026, the day it was discovered. "We discovered that an unauthorized third party accessed the server that runs our web-based File Manager at files.dreamhost.com," reads the notification. "The unauthorized third party exploited a vulnerability in that software and was able to access the FTP/SFTP usernames and passwords of individuals who logged into this File Manager between approximately August 2026 and October 23, 2026."
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