All tracked items across vulnerabilities, news, research, incidents, and regulatory updates.
Cisco Secure Firewall Management Center (FMC) and Cisco Security Cloud Control (SCC) contain a deserialization of untrusted data vulnerability (a flaw where the software unsafely processes data that could contain malicious code) in their web management interfaces. An unauthenticated attacker (someone without login credentials) can remotely execute arbitrary Java code with root privileges (the highest level of system access) on affected devices. This vulnerability is currently being actively exploited by attackers.
Fix: Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable. The deadline for remediation is 2026-03-22.
CISA Known Exploited VulnerabilitiesResearchers successfully ran a very large AI model (Qwen 397B, a Mixture-of-Experts model where each response only uses a subset of the total weights) on a MacBook Pro by using Apple's "LLM in a Flash" technique, which stores model data on the fast SSD storage and pulls it into RAM as needed rather than keeping everything in memory at once. They used Claude to run 90 experiments and generate optimized code that achieved 5.5+ tokens per second (response speed) by quantizing (reducing precision of) the expert weights to 2-bit while keeping other parts at full precision. The final setup used only 5.5GB of constant memory while streaming the remaining 120GB of compressed model weights from disk on demand.
MLflow, a machine learning platform, has a vulnerability (CVE-2025-15031) in how it extracts model files from compressed archives. The issue is that the software uses `tarfile.extractall` (a Python function that unpacks compressed tar files) without checking whether file paths are safe, allowing attackers to use specially crafted archives with `..` (parent directory references) or absolute paths to write files outside the intended folder. This could let attackers overwrite files or execute malicious code, especially in shared environments or when processing untrusted model files.
Mesop contains a critical vulnerability in its testing module where a `/exec-py` route accepts Python code without any authentication checks and executes it directly on the server. This allows anyone who can send an HTTP request to the endpoint to run arbitrary commands on the machine hosting the application, a flaw known as unauthenticated remote code execution (RCE, where an attacker runs commands on a system they don't own).
Mesop has a path traversal vulnerability (a technique where an attacker uses sequences like `../` to escape intended directory boundaries) in its file-based session backend that allows attackers to read, write, or delete arbitrary files on the server by crafting malicious `state_token` values in messages sent to the `/ui` endpoint. This can crash the application or give attackers unauthorized access to system files.
A story claimed that ChatGPT helped cure an Australian entrepreneur's dog of cancer, generating widespread attention as evidence that AI could revolutionize medicine. However, the article suggests this narrative is more complicated than the promoted version, implying the reality behind the claim differs from what was publicly reported.
The h3 library has a vulnerability in its Server-Sent Events (SSE, a protocol for pushing real-time messages from a server to connected clients) implementation where newline characters in message fields are not removed before being sent. An attacker who controls any message field (id, event, data, or comment) can inject newline characters to break the SSE format and trick clients into receiving fake events, potentially forcing aggressive reconnections or manipulating which past events are replayed.
SiYuan's Bazaar (a community marketplace for plugins and themes) renders package README files without sanitizing HTML, allowing malicious package authors to embed JavaScript that runs when users view package details. Because SiYuan runs on Electron (a framework for building desktop apps) with `nodeIntegration: true` (allowing JavaScript to access system-level commands), this vulnerability escalates from XSS (cross-site scripting, where attackers inject malicious code into web pages) to full remote code execution (the ability to run any command on the user's computer).
Researchers discovered three connected flaws in Claude (an AI assistant) that can work together to steal user data, starting with a prompt injection attack (tricking the AI by hiding malicious instructions in its input) combined with a Google search vulnerability. This attack chain could potentially compromise enterprise networks that rely on Claude.
The @aborruso/ckan-mcp-server tool allows attackers to make HTTP requests to any address by controlling the `base_url` parameter, which has no validation or filtering. An attacker can use prompt injection (tricking the AI by hiding instructions in its input) to make the tool scan internal networks or steal cloud credentials, but exploitation requires the victim's AI assistant to have this server connected.
Langflow has a security flaw called IDOR (insecure direct object reference, where an attacker can access or modify resources belonging to other users) in its API key deletion feature. An authenticated attacker can delete other users' API keys by guessing their IDs, because the deletion endpoint doesn't verify that the API key belongs to the person making the request. This could allow attackers to disable other users' integrations or take over their accounts.
Nvidia has released DLSS 5, a new 3D guided neural rendering model (an AI system that generates realistic graphics in real-time) that can alter a game's lighting and materials during gameplay. Many gamers have criticized the technology for changing how games look in ways they didn't expect, with complaints that it distorts character appearances and doesn't respect the original artists' creative vision.
Magecart attacks (malicious code injected into e-commerce sites to steal payment data) often hide in third-party resources like images or scripts that never enter a company's code repository, making them invisible to static analysis tools like Claude Code Security. Claude Code Security is designed to scan code you own, so it cannot detect malicious code injected at runtime through compromised external libraries, CDNs (content delivery networks that distribute files globally), or data hidden in binary files like favicons, which means repository-based scanning has a fundamental blind spot for this attack class.
AI agents, like the open-source Clawdbot, are becoming more powerful and autonomous but introduce serious security risks because attackers can compromise them through the open-source supply chain. Two major attack types threaten AI systems: model file attacks (where malicious code is hidden in AI model files uploaded to trusted repositories) and rug pull attacks (where attackers compromise MCP servers, which are tools that give AI agents capabilities, to perform malicious actions). The article notes that traditional security methods don't yet exist for protecting AI agents, and a single corrupted component can spread threats across many teams.
Fix: The source explicitly recommends: 'Teams must scan model files with tools that can parse machine learning formats, and load models in isolated containers, virtual machines or browser sandboxes.' For rug pull attacks specifically, the article states that 'the alternative is to use remote MCP servers whose code is maintained by trusted organizations' like GitHub, which 'reduces the risk of an MCP rug pull attack' (though it does not prevent malicious actions from the tools themselves).
Palo Alto Unit 42This research paper presents a method for searching location-based services (apps that use your geographic position, like finding nearby restaurants) while protecting user privacy and ensuring the results are trustworthy. The approach combines spatio-temporal (location and time-based) keyword searching with verifiability (a way to prove the results are correct), allowing users to query location services without exposing their exact location or search patterns to the service provider.
Fix: Update to SiYuan version 3.5.10 or later. The vulnerability affects SiYuan <= 3.5.9.
GitHub Advisory DatabaseShadow AI refers to AI systems hidden within SaaS applications (software services accessed online) that operate without proper oversight, creating security risks that can lead to major data breaches. The article emphasizes that organizations lack visibility into these autonomous AI systems and calls for better monitoring and control mechanisms to manage agentic AI (AI that can independently take actions to achieve goals).
Diffusion models (AI systems that generate images and other content by gradually removing noise from random data) are vulnerable to backdoor attacks, where hidden triggers cause the model to produce harmful outputs. Researchers created PureDiffusion, a framework that can both defend against these attacks by detecting and inverting the hidden triggers, and amplify attacks by making existing backdoors more effective.
This paper addresses backdoor attacks (where attackers secretly poison AI models to make them behave maliciously) in vertical federated learning (VFL, a setup where different organizations train an AI together on their own private data). The researchers propose a defense using a latent masked autoencoder (LMAE, a type of neural network that detects patterns and missing information) to identify when one participant is submitting suspicious, inconsistent data compared to honest participants, allowing the system to reject malicious contributions.
Fix: The paper proposes a novel defense mechanism using a latent masked autoencoder (LMAE) to assess the semantic consistency of embeddings (learned data representations) from different participants. The authors developed an algorithm based on the LMAE that identifies attackers and enables backdoor-resistant predictions. The defense was tested on multiple datasets and backdoor attack types and demonstrated effectiveness at identifying attackers while maintaining high prediction accuracy.
IEEE Xplore (Security & AI Journals)Fix: The source explicitly recommends: (1) Validate `base_url` against a configurable allowlist of permitted CKAN portals, (2) Block private IP ranges (RFC 1918, link-local addresses like 169.254.x.x), (3) Block cloud metadata endpoints (169.254.169.254), (4) Sanitize SQL input for datastore queries, and (5) Implement a SPARQL endpoint allowlist.
GitHub Advisory DatabaseFix: Modify the delete_api_key endpoint and function by: (1) passing current_user to the delete function; (2) adding a verification check in delete_api_key() that confirms api_key.user_id == current_user.id before deletion; (3) returning a 403 Forbidden error if the user doesn't own the key. Example code provided: 'if api_key.user_id != user_id: raise HTTPException(status_code=403, detail="Unauthorized")'
GitHub Advisory DatabaseThe Pentagon is planning to create secure environments where AI companies can train their models on classified military data, which would embed sensitive intelligence like surveillance reports into the AI systems themselves and bring these companies closer to classified information than before. This represents a major shift from current use of AI models like Claude in classified settings, but introduces unique security risks by allowing models to learn from rather than just access classified data.
Reco, a SaaS security platform, launched "Reco AI Agent Security" on March 18 to address "agent sprawl," the problem of autonomous AI agents (like Copilot and ChatGPT integrations) accessing sensitive data and taking actions across multiple systems without human oversight. The new tool gives security teams visibility and control over these AI agents by using behavior-based detection (analyzing API call patterns and workflow signatures) instead of traditional connection-based methods, identifying risks like agents with excessive permissions or misconfigured access to customer data.
Fix: Reco AI Agent Security is explicitly designed as the mitigation. According to the source, the offering provides: (1) AI agent discovery through multi-layered detection that analyzes API call patterns and service account activity to identify autonomous behavior; (2) risk analysis by correlating activity across applications and recognizing workflow signatures of automation tools like n8n, Zapier, and Make; and (3) governance and control over all AI agents operating in the SaaS ecosystem. The platform tracks OAuth connections, analyzes decision-making patterns that indicate autonomous action, and monitors cross-application activity to identify agents that traditional SSPM tools miss.
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