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
Some WordPress sites can still be attacked while they install the fix for an actively exploited flaw, according to Umut Bayram, the Picus Security researcher who found it. The update that installs the fix still unpacks its files using the site's old, unpatched code, so an attacker who strikes at the right moment can run code on the server. The researcher advises blocking web access to the
Blockchain intelligence firm Chainalysis says a recent attack on its systems and internal tools has exposed credentials and live tokens for all customers. Chainalysis provides blockchain analysis tools to trace cryptocurrency transactions and investigate crimes such as fraud, ransomware, and money laundering. Its customer list includes law enforcement, government agencies, financial institutions, insurance companies, and cryptocurrency businesses worldwide. This follows the company's disclosure last Friday that attackers using AI tools accessed customers' names, email addresses, user IDs, and organizational role labels using what it described as "offensive agentic techniques." While it initially said the impact was limited and the breach affected only non-sensitive identity metadata, later updates revealed that usernames and password hashes for all customers were exposed in an "on-path attack" (more commonly known as a Man-in-the-Middle or MITM attack).
A browser extension that can run scripts on claude.ai could make Claude Desktop run code on a person's computer, researchers at Forever Security, an endpoint security company, showed in a test. The attack needs Dispatch, a Claude feature that lets people send tasks from their phone to Claude Desktop. The flaw that makes this possible can still be exploited, the researchers said earlier this
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)
Blockchain analysis firm Chainalysis has revealed that its ongoing investigation into the agentic cyber attack targeting its systems also exposed names, usernames, password hashes and live tokens for all users. "While we investigated, we removed access to Reactor and Data Solutions," the company said. "At this time we continue to have no evidence that any investigations data has been accessed
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.
A cybersecurity incident forced UNM Health to divert ambulances, cancel appointments, and pause care while it contained the threat. On Monday, October 26, the University of New Mexico Health System (UNM Health) faced the kind of security incident that healthcare leaders dread: one serious enough to change how patients could receive emergency care. A major hospital network in New Mexico worked urgently on Monday to fix a cybersecurity problem. The issue forced it to send ambulances to other hospitals and left the state without a hospital emergency department able to treat the most serious injuries. UNM Health operates New Mexico’s only Level 1 trauma center. These centers treat the most serious and complex injuries. Even a temporary closure can put extra pressure on other hospitals. UNM Health runs New Mexico’s only Level 1 trauma center. That matters because a Level 1 center handles the most complex injuries, and losing even temporary access to it puts pressure on every nearby hospital. The organization told patients not to come to its main Albuquerque hospital and canceled appointments across more than 30 clinics. “The University of New Mexico Health System is experiencing a cybersecurity issue at this time. UNM Health officials are actively working to resolve this issue. Currently, all ambulance traffic is being diverted to other hospitals and all appointments at all clinics today have been cancelled. UNM Health patients should not show up to UNM Health facilities on October 26. Emergencies should be handled by 911 and non-emergencies should be delayed until further notice.” reads the update published by the organization on October 26, 2026. “The cybersecurity threat is affecting the entire University of New Mexico Health Sciences Center. “ UNM said its IT security teams identified the threat around 11 a.m. and began containment work. They later said the event affected some end-user devices but did not reach core systems or servers. Leadership activated an Emergency Operations Center, brought in outside support, and notified state and federal law enforcement. That is the right sequence: isolate first, investigate second, communicate continuously. From 12:30 to 2:30 p.m., UN Health temporarily stopped accepting new patients while teams protected the network. “System outages also required the suspension of outpatient surgeries and appointments for the remainder of the day.” This was not an abstract IT problem. Patients arrived for care and were told to reschedule. Staff had to explain, repeatedly, why a hospital visit could not proceed as planned. UNM also canceled classes at its Health Sciences Center and sent nonessential employees home. Medical, nursing, pharmacy, and population health students lost a day of instruction because the institution had to protect its network. Other providers stepped in. Presbyterian Healthcare Services and Lovelace Health System coordinated with UNM and took additional patients, which is exactly what a functioning regional healthcare system should do. UNM Health began restoring its systems on Monday afternoon. By the following day, hospitals, ambulance services and clinics were back to normal. The incident is still under criminal investigation. Employees reported messages claiming that patient, student and staff data had been stolen, but UNM has not confirmed this. The hospital also warned people to watch out for phishing and fraud attempts by attackers exploiting the incident. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, UNM Health)
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