Bulletin ID: 2026-071-AWS Scope: AWS Content Type: Important (requires attention) Publication Date: 08/03/2026 12:30 PM PDT Description: AWS Command Line Interface (AWS CLI) is a unified tool to manage AWS services from the command line. We identified CVE-2026-18654, an issue where the EMR SSH helper commands (aws emr ssh, aws emr socks, aws emr put, aws emr get) disabled SSH host key verification, which might allow man-in-the-middle actors to intercept SSH sessions and file transfers via network positioning between the client and the EMR cluster endpoint. Impacted versions: - AWS CLI v1 <= 1.45.27 - AWS CLI v2 <= 2.35.2 Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.
Tenable spent 30 days running frontier AI models against our own code. It didn’t just find bugs — it proved they’re real, with reproducible exploits. That fundamentally changes code security from ranking potential code defects to a much higher signal focused on the findings that matter. Read on to learn how it reshaped our security team's work, what it cost, and why your program is next. Key takeaways: Now code security starts with proof, not suspicions. Frontier AI instantly builds working exploits and proves which flaws are genuinely dangerous in your source code. Now remediations are confirmed issues, not just ranked lists of maybes. The durable asset is the harness, not the model. Frontier AI models get the attention, but the durable asset for security teams is the harness: the orchestration and systems around the model that turn suspected flaws into proven, reproducible exploits engineers can act on. Frontier AI doesn’t replace senior researchers; it makes one as productive as five. The scarce resource is still the expert who writes the threat model and judges what’s real. Buy the compute without funding that person, and you get a very fast way to generate findings no one can use. We’ve been running Claude Mythos Preview against our own code now for over 30 days, and one thing is crystal clear: Code security is fundamentally changing, and we believe there’s no turning back. At Tenable, our security team already had security testing agents that drove our applications, exercised API endpoints, and ran our predefined checks. But until recently, the agents couldn’t handle the harder part of code testing: finding previously unidentified flaws and proving their exploitability. As part of our work testing Anthropic’s Claude Mythos Preview for Project Glasswing , we built an agentic code security harness and powered it with this Anthropic frontier LLM, running it against our code and service repositories with pinned commits to ensure reproducible results. The work of code security is changing, but not in the way hype-driven blogs suggest. And certainly not for free. We’ve found the costs are measured in two currencies: dollars and senior-engineer hours. What follows is an account from the security practitioner’s perspective of Tenable’s internal security team and how we leveraged frontier AI: where the model proved its value, where it fell short, and what you should consider before investing further. From ranking guesses to ranking proof with frontier AI For well over a decade, the scarce resource for security teams was analyst attention. We built a whole discipline around it — reachability heuristics, exploitability guesswork, etc. — all to decide what a human security analyst should look at first in your own code. Now a frontier AI model in a harness collapses that. The ranking doesn’t go away, it gets a proof instead of a guess. When a finding arrives with a working exploit, you’re no longer ranking by how likely it is to matter; you’re ranking by what you’ve already proven does. When an LLM can surface a suspected flaw and drive a working exploit against a running build, the difficult question is no longer “which among the thousands of static findings deserves a human first?” It becomes “which of the code defects are real, and can we prove it?” A bug that shows up with a reproducible proof-of-concept sorts itself. One that can’t be reproduced goes to a validation queue. This does not mean fewer bugs. In fact, it means many more findings, especially early on, because the model surfaces threats and exposures in your source code that traditional tooling never would. Instead, what changes with frontier AI is that practically all of the bugs that reach a human analyst arrive pre-sorted by proof instead of by score. It becomes a short list of things you can already reproduce, plus a holding pen of candidates the harness is still chewing on. The model gets the headlines; the harness does the work This is not to say that the model do…
This entry discusses the challenge of obtaining publicly available post-infection network traffic captures (PCAPs) showing command-and-control (C2) beaconing and data exfiltration from compromised hosts. While pre-infection traffic sources such as malware delivery URLs and exploit attempts are readily accessible, sustained post-infection traffic captures are scarce or limited to older datasets, small-scale analyst investigations, or short sandbox runs. The community-driven abuse.ch platform provides extensive threat intelligence but does not currently offer continuous post-infection PCAP data. The discussion highlights the difficulty in sourcing long-duration post-infection captures for research and detection development.
This is an open-source PowerShell script designed to harden Windows 11 systems by leveraging native OS security features. It restricts PowerShell script execution, disables Windows Script Host, closes remote management ports, disables SMBv1, enables Microsoft Defender Attack Surface Reduction rules, and disables hidden administrative shares. The script aims to reduce the attack surface against ransomware, phishing, lateral movement, and network worms without relying on third-party software.
A critical vulnerability in the Active Storage framework can allow an unauthenticated attacker to read arbitrary files from a Rails application, and potentially escalate to remote code execution (RCE). [...]
The flaw can be exploited by unauthenticated attackers to read arbitrary files and potentially achieve remote code execution (RCE). The post Ruby on Rails Patches Critical Vulnerability appeared first on SecurityWeek .
Bulletin ID: 2026-085-AWS Scope: AWS Content Type: Important (requires attention) Publication Date: 08/20/2026 13:30 PM PDT Description: Improper input validation in the Time Series Visual Builder (TSVB) plugin in OpenSearch Dashboards before 3.8 allows a remote authenticated user with standard data access permissions to execute arbitrary code on the server by sending a crafted JSON payload to the metrics visualization API endpoint. To mitigate this issue, users should upgrade to OpenSearch Dashboards 3.8 or later. Impacted products and versions: - OpenSearch-Dashboards (open-source, self-managed): >=3.0.0, =3.0.0, <3.8.0 Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.
A Chinese-speaking threat actor is using the DeepSeek AI model and the open-source Hermes Agent to conduct autonomous cyberattacks on exposed servers with limited human involvement. [...]
Noteworthy stories that might have slipped under the radar: parcel delivery company OnTrac hacked, Adobe patches, UK Department for Education loses 607,000 records. The post In Other News: OpenAI Open Source Tool, AWS Links Hacks to North Korea, Mythos Crypto Research appeared first on SecurityWeek .
When the AI Act comes into force, AI companies will be required to make clear to consumers with labels or digital watermarks that chatbots or imagery are generated with AI. The post EU to Crack Down on AI Deepfakes, Illicit Imagery and Hacking With New Team in Brussels appeared first on SecurityWeek .
Hugging Face PyTorch Image Models checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face PyTorch Image Models. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.
The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-27987.
Wolf CMS versions up to 0.8.3.1 contain a remote code execution vulnerability in the FileManagerController. Authenticated users with the file_manager_mkfile capability can create arbitrary PHP files without proper extension validation. These files can be placed in web-accessible directories and executed via HTTP requests, enabling remote code execution.
Overview Six vulnerabilities have been discovered within the SGLang project, including remote code execution (RCE), server-side request forgery (SSRF), local file read, credential leakage, and model weight exfiltration on a target server. Exploitation does not require authentication in most cases, and some vulnerabilities require only network access with no API keys or user credentials. At the time of publication, no patches are available from the project maintainers, and coordination attempts have been unsuccessful. Description SGLang is an open-source framework for serving large language models (LLMs) and multimodal AI models, supporting models such as Qwen, DeepSeek, Mistral, and Skywork, and is compatible with OpenAI APIs. Six vulnerabilities have been discovered within the tool and are tracked as follows: CVE-2026-15969 SGLang contains unauthenticated RCE in /load_lora_adapter_from_tensors by bypass of SafeUnpickler’s incomplete denylist, allowing arbitrary command execution through crafted base64-encoded pickle payloads. CVE-2026-15971 SGLang contains an RCE vulnerability when the optional dumper subsystem is enabled, which allows for a sandbox escape when DUMPER_SERVER_PORT is set, enabling code execution on inference requests. CVE-2026-15974 SGLang contains an SSRF and local file read in the multimodal generation endpoint /v1/chat/completions because image_url input is unsanitized, allowing access to internal metadata, secrets, and services. CVE-2026-15976 SGLang contains a RCE vulnerability when attempting to load model weights from a HuggingFace repository, specifically within the /update_weights_from_disk , where torch.load(..., weights_only=False) fallback enables pickle deserialization of .bin files. CVE-2026-15977 SGLang contains a credential leakage vulnerability in the /server_info endpoint, which returns API keys and SSL keyfile information when only the --admin-api-key is configured. CVE-2026-15978 SGLang contains a model weight exfiltration vulnerability when no API keys are configured, because SGLang will expose two endpoints that allow a remote attacker to trigger distributed weight broadcasting using NCCL and then triggering data transfer, attackers can exfiltrate all model weights. Impact If exploited, these vulnerabilities could allow an unauthenticated attacker to achieve remote code execution, exfiltrate model weights, or overwrite arbitrary files on the host machine running SGLang. Deployments that expose the affected interface to untrusted networks are at the highest risk of exploitation. Solution Until a patch is available, affected users should consider the following mitigations: Mitigations Restrict access to the service interfaces and ensure they are not exposed to untrusted networks. Implement network segmentation and access controls to prevent unauthorized interaction with the vulnerable endpoints. Change SGLANG_USE_PICKLE_IPC to "false" within environ.py . Disable endpoints not in use to remove potential attack vectors. The SGLang maintainers have begun addressing pickle deserialization vulnerabilities and are working to refactor the code base with msgpack to prevent deserialization issues such as CVE-2026-14890, but the SGLANG_USE_PICKLE_IPC defaults to true within the codebase at the time of writing. Acknowledgements Thanks to the reporter, Apoorv Dayal [apoorvdayal@outlook.com]. This document was written by Christopher Cullen. Vendor Information One or more vendors are listed for this advisory. Please reference the full report for more information. References https://github.com/sgl-project/sglang/security/advisories/GHSA-2wvm-gjg7-5jfm https://github.com/sgl-project/sglang/security/advisories/GHSA-h6rf-77vv-9mvj https://github.com/sgl-project/sglang/security/advisories/GHSA-wf98-gv64-5wrf https://github.com/sgl-project/sglang/security/advisories/GHSA-x7w5-h7rp-gfp9 https://github.com/sgl-project/sglang/security/advisories/GHSA-jx7q-p32r-7wx8 https://github.com/sgl-project/sglang/security/a…
As federal enforcement tightens and states begin stepping in with their own cybersecurity mandates, water and wastewater utilities face a looming wave of hard compliance deadlines, compounded by recent cyber attacks on state water utilities. Key takeaways While the EPA’s national sanitary-survey mandate stalled in court, the agency is aggressively using existing authority, technical guidance, and enforcement alerts to inspect cyber gaps. Community water systems serving 3,301 to 49,999 people, the vast majority of U.S. systems, must certify their Risk and Resilience Assessments (RRAs) by June 30, 2026, under AWIA 2013. New York has already finalized binding cybersecurity regulations for wastewater facilities, setting a regulatory template that other states are expected to follow in 2026 and 2027. Under CIRCIA, utilities will soon be legally required to report significant cyber incidents to CISA within 72 hours and ransom payments within 24 hours. Federal grant programs (SLCGP) and liability protections have been extended through Sept. 30, 2026, but remain tied to unpredictable budget cycles while targeted cyber threats continue to rise. Navigating the new reality of water cyber regulation In 2023, the U.S. EPA made an initial push to fold cybersecurity evaluations into state sanitary surveys. While that effort was stayed in court and subsequently withdrawn, the underlying federal statutory requirements and enforcement drivers remain fully active. Instead of relying on new survey rules, federal and state regulators are actively using existing statutory authority and technical guidance to shift water cybersecurity from voluntary recommendations to enforceable compliance deadlines. The urgency to strengthen cybersecurity for water facilities is underscored by a recent coordinated cyber attack that disrupted water and wastewater utility operations across more than 30 Minnesota communities in late July 2026. Utility cyber regulations and mandates moving forward America’s Water Infrastructure Act (AWIA) 2013 / Safe Drinking Water Act (SDWA) 1433 is still very much in force. Community water systems serving more than 3,300 people are legally required to certify a Risk and Resilience Assessment (RRA) and Emergency Response Plan (ERP) to EPA on a five-year recertification cycle, and that cycle explicitly covers cyber threats, not just physical and natural hazards. Recertification deadlines: Systems serving 100,000-plus people: March 31, 2025 50,000–99,999 tier: Dec. 31, 2025 3,301–49,999 tier, the vast majority of U.S. water systems: June 30, 2026, with ERPs due six months after. The EPA hasn’t stopped pushing on cyber. It’s just doing it through guidance, technical assistance, and enforcement of existing authority rather than new rulemaking. In May 2024, the EPA issued an enforcement alert warning it would step up inspections tied to cybersecurity gaps found in drinking water systems. On Oct. 23, 2025, the EPA released an updated package of cyber tools: Revised Emergency Response Plan guide Cybersecurity Incident Response Plan (CIRP) template Incident-specific checklists Cybersecurity procurement checklist These tools are designed to help utilities fold cybersecurity directly into the RRA/ERP process they’re already required to complete. States are stepping in where EPA stepped back With the EPA’s national sanitary-survey mandate dead, states have started writing their own cybersecurity rules for water systems. New York is the clearest example: In March 2026, the New York State Department of Environmental Conservation finalized amendments to six New York Codes, Rules and Regulations (NYCRR) Parts 616, 650 and 750 , adding binding cybersecurity regulations for wastewater treatment facilities, including mandatory incident reporting and access-control requirements built around EPA’s own cybersecurity guidance, incorporated into the rule by reference. Reporting requirements took effect March 26, 2026. It’s a template other states are watchi…
Canada’s new Critical Cyber Systems Protection Act (Bill C-8) introduces a strict 72-hour cyber incident reporting mandate. Find out how Tenable is helping critical national infrastructure operators bridge the IT/OT divide to ensure full compliance. Key takeaways: Bill C-8 introduces stringent new cyber incident reporting requirements and heavy financial penalties for critical infrastructure operators. Eliminating network blind spots with a hybrid IT/OT discovery approach, including Safe Active Querying for isolated, hard-to-reach process-control systems, enables operators to establish a required security baseline. Predictive Vulnerability Priority Rating (VPR) scoring helps you prioritize and focus limited resources on the critical flaws that actually threaten physical safety and uptime. Advanced multi-detection engines and seamless IT workflow integrations accelerate mean-time-to-respond (MTTR) to help both security teams and operators align with a strict 72-hour reporting requirement. With the enactment of Canada’s Critical Cyber Systems Protection Act (CCSPA), commonly known as Bill C-8, the Canadian federal government is laying down a clear framework to protect the cyber-physical systems that are vital to national critical infrastructure security. For designated operators in telecommunications, energy, transportation, and banking, the mandate is clear: Establish formalized cybersecurity programs, mitigate supply chain risks, and — most critically — report cyber incidents to authorities within 72 hours. Failure to comply carries heavy consequences, including penalties that can reach up to $15 million Canadian dollars (CAD). But beyond the threat of fines, Bill C-8 highlights a fundamental operational challenge that many industrial organizations are still struggling to solve: How can you detect, investigate, and report a breach in 72 hours when you lack unified visibility across your converged IT and OT environments? Requirements for meeting Bill C-8's 72-hour incident reporting mandate In modern industrial operations and critical infrastructure, the line between IT and OT continues to blur. The introduction of connectivity (e.g., IoT-connected cameras and building management systems) has optimized processes and service delivery, but it has also introduced new cyber exposures. Today, threat actors do not honor traditional network silos; they frequently compromise a web-facing IT asset or IoT device and move laterally into the operational technology (OT) environment to disrupt physical processes. Meeting a 72-hour incident reporting window is nearly impossible if your security team is relying on fragmented point solutions. Solutions that focus exclusively on passive OT network monitoring often leave massive blind spots — especially considering that IT and IoT devices can constitute up to 50% of an industrial environment. When an incident occurs, teams waste precious hours manually correlating alerts across disconnected tools rather than actively investigating the root cause. To comply with CCSPA and protect uptime, critical national infrastructure (CNI) operators must bridge the IT/OT security divide. Establish your CCSPA cybersecurity baseline The CCSPA requires operators to implement formalized cybersecurity programs. The foundation of any mature security program is a comprehensive asset inventory — you cannot secure what you cannot see. The Tenable One Exposure Management Platform helps organizations eliminate security blind spots by building a complete, unified inventory of all OT, IoT, and IT assets. Tenable goes beyond passive-only network monitoring with our proprietary Safe Active Query technology. This hybrid approach safely communicates with industrial devices in their native protocols to uncover significantly more assets than passive monitoring alone — including dormant process control systems, shadow IT, and unmanaged IoT — without disrupting process integrity or impacting equipment uptime. Prioritize what matter…
This vulnerability allows remote attackers to execute arbitrary code on affected installations of GIMP. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The ZDI has assigned a CVSS rating of 7.8. The following CVEs are assigned: CVE-2026-18300.
This vulnerability allows remote attackers to execute arbitrary code on affected installations of GIMP. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The ZDI has assigned a CVSS rating of 7.8. The following CVEs are assigned: CVE-2026-18301.
This vulnerability allows remote attackers to execute arbitrary code on affected installations of GIMP. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The ZDI has assigned a CVSS rating of 7.8. The following CVEs are assigned: CVE-2026-18302.
This vulnerability allows remote attackers to execute arbitrary code on affected installations of GIMP. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The ZDI has assigned a CVSS rating of 7.8. The following CVEs are assigned: CVE-2026-18303.
