Active AI Security Signals

According to Sand Security’s WriteOut research and subsequent reporting, Writer’s agent live preview feature had a critical session isolation flaw that forwarded a logged‑in user’s session cookie into an attacker‑controlled sandbox when a malicious preview link was opened.[1][2] This allowed cross‑tenant account hijacking: replayed session tokens could grant access to private chats, documents, agents, configurations, private models, connectors, and LLM credentials, and in some cases full administrative control.[1][2] RealGround analysis: this is a SaaS AI platform risk centered on weak session isolation and unsafe agent preview architecture, showing that AI agent UX features can become high‑impact account takeover vectors across tenants. Organizations using AI SaaS should assess session/token handling in agent features, adopt stricter origin and sandbox isolation, and continuously red‑team agent flows to detect similar cross‑tenant compromise paths before exploitation.

The article describes a Microsoft 365 device code phishing campaign using custom DEBULL tooling and collaboration-themed lures to trick users into completing the legitimate Microsoft device code login flow, allowing attackers to take over M365 accounts without a fake password page.[1][8] According to ZeroBEC, victims are redirected to a compromised website that orchestrates the device code challenge chain, abusing OAuth 2.0 device code flow to obtain tokens and access emails, files, chats, and other SaaS data.[1][2][4] From a RealGround perspective, this is a SaaS identity and access risk that directly affects AI-enabled M365 workloads (Copilot, automation agents, chatbots) by giving attackers valid tokens they can use to operate as the user inside those services. Organizations should apply conditional access policies to block or tightly scope device code flow, monitor OAuth grants and anomalous activity, and use continuous red teaming to test AI and SaaS integrations against this class of token-stealing phishing attacks.[2][3][4][5][9]

The reported BeyondTrust flaws are critical pre-authentication vulnerabilities in Remote Support (RS) and Privileged Remote Access (PRA) that allow unauthenticated remote attackers to execute OS commands or bypass authentication, leading to full system compromise on exposed management appliances.[2][4] According to advisories, these products are widely deployed, internet-facing in many environments, and used for privileged access and remote administration, making them high-value targets for attackers.[2][4] From a RealGround perspective, any AI agents or LLM-based operations that rely on BeyondTrust RS/PRA as part of their privileged access, support workflows, or MLOps infrastructure inherit this risk through the AI supply chain: compromise of these tools could give attackers a pathway to AI backends, model servers, or sensitive data stores controlled via those remote access channels. Practically, organizations should inventory and patch all RS/PRA instances, incorporate these components into SBOMs and AI system diagrams, and enforce network segmentation and least-privilege controls around remote access tools that interact with AI infrastructure to prevent a single appliance exploi

Report facts: The ToddyCat APT group is using a new Umbrij malware tool to hijack OAuth tokens and abuse the Google API to covertly access corporate Gmail accounts, focusing on API-based access to email communications hosted on Gmail.[1][2][6] This reflects a broader tactic where ToddyCat steals OAuth 2.0 tokens and browser session data at scale to reach cloud email and other SaaS services outside the initially compromised infrastructure.[3][4][10] RealGround analysis: For AI-enabled organizations, similar OAuth abuse and session hijacking techniques can be used to gain unauthorized access to AI-powered SaaS platforms (e.g., email copilots, workflow agents, or LLM-integrated productivity suites), enabling data exfiltration and covert manipulation of AI-driven business processes. Security teams should continuously red team OAuth and API integrations, assess SaaS and AI-agent access models, and implement strong governance around token handling, conditional access, and anomaly detection for API-driven access to email and AI services.

The article reports that CISA has added Microsoft SharePoint Server remote code execution vulnerability CVE-2026-45659 (CVSS 8.8) to its Known Exploited Vulnerabilities catalog after evidence of active exploitation, affecting on‑prem SharePoint Server Subscription Edition, 2019, and Enterprise 2016 through deserialization of untrusted data.[1][3][4] Microsoft’s advisory notes that any authenticated low‑privilege user (e.g., Site Member) can remotely execute arbitrary code without admin rights or user interaction, and U.S. federal agencies are ordered to patch urgently.[1][4][5] From a RealGround perspective, AI and agent platforms that integrate with or depend on SharePoint for data access, knowledge bases, or workflow orchestration inherit this RCE risk: compromise of SharePoint can lead to downstream data leakage, manipulation of documents used to ground AI outputs, and abuse of AI agents that trust SharePoint as a canonical source. Organizations should treat vulnerable SharePoint instances as a critical part of their AI ecosystem, ensure rapid patching and build verification, and include these systems in AI security readiness assessments and threat models, particularly where AI

The article reports that Microsoft is adding new Teams admin controls that detect likely external AI bots joining meetings and force them into the lobby, where organizers must explicitly approve or deny their entry, with policies configurable at org, group, or user level.[2][7] This increases visibility over automated participants and reduces accidental admission of unapproved AI meeting assistants in a critical collaboration SaaS platform.[2] From a RealGround perspective, this highlights SaaS AI risk around third‑party and external bots in collaboration tools, and the need for clear policies on approved AI agents, business logic audits of meeting bots, and readiness assessments to ensure that lobby controls, vendor allowlists, and user training are aligned with organizational security and compliance requirements.[1][2] It also underscores the importance of designing and securing internal AI agents so they behave predictably under these new controls and cannot be abused to gain unauthorized access to sensitive meetings.[4][6]

According to SecurityWeek, Nebulock is a cybersecurity startup that raised $25M Series A funding to build an AI-native contextual security platform that turns enterprise activity into a behavioral system of record and delivers autonomous, vendor-agnostic threat hunting and behavioral analytics across endpoints, identity, and cloud.[1][4][5] The platform operates as a SaaS-style, AI-powered threat hunting and detection environment focused on proactive detection and continuous monitoring of enterprise environments.[2][5] From a RealGround perspective, such AI-native SaaS security platforms both expand the attack surface (through complex AI-driven analytics, multi-tenant data, and integration with many parts of the security stack) and become high-value targets whose compromise could expose behavioral records, detection logic, and integrated telemetry. Organizations adopting Nebulock-like services should assess AI-specific SaaS risks, including data handling, model governance, and resilience of autonomous threat hunting logic, and continuously red-team these AI-driven controls to validate that they behave securely under adversarial conditions.

SecurityWeek reports that the ShinyHunters campaigns rely heavily on stolen credentials, compromised OAuth tokens, vishing, and abuse of legitimate access to cloud and SaaS ecosystems, rather than malware or zero-days.[2] The article highlights that attackers are increasingly targeting identities, authentication workflows, SaaS integrations, and trusted access paths, demonstrating that a single trusted login or overlooked permission can enable substantial data theft and extortion.[2] From a RealGround perspective, AI-powered and SaaS-integrated agents are exposed to the same identity- and OAuth-centric attack paths, making hardening of authentication flows, token governance, and third-party integrations critical to prevent agent takeover or data exfiltration via connected apps. Organizations should apply continuous red teaming of AI/SaaS workflows, rigorously audit AI agent business logic and permissions, and manage the AI supply chain and OAuth-based integrations as first-class security surfaces.

According to the article, the main risk from shadow AI has shifted from employees pasting sensitive data into public LLMs toward uncontrolled access control as AI agents gain direct connections to SaaS apps, APIs, credentials, and enterprise systems.[1] The piece emphasizes that many organizations lack even a basic inventory of where agents live, what resources they touch, what identities and secrets they use, and whether dormant agents still retain active permissions, creating persistent exposure.[1] From a RealGround perspective, this represents a SaaS AI risk centered on unmanaged agent identities and over-privileged integrations, meaning organizations need continuous discovery, testing, and hardening of AI agent behaviors across SaaS and cloud environments. Practically, applying Continuous AI Red Teaming to agentic workflows and their connected SaaS services can help identify excessive permissions, risky automation paths, and dormant-but-active agents before they are abused.

The report describes DragonForce ransomware operators using a custom Go-based RAT, Backdoor.Turn, to tunnel command-and-control traffic through legitimate Microsoft Teams TURN relay infrastructure, making malicious traffic appear as normal Teams connections.[1][2][9] Security products and defenders therefore primarily see outbound connections to trusted Microsoft Teams servers, complicating detection and response.[2][3] For RealGround, the key implication is that AI-enabled or collaboration-integrated SaaS environments (including AI copilots or bots embedded in Teams) are exposed to abuse of underlying SaaS transport and identity mechanisms for stealthy C2 and persistence; organizations need to harden network egress controls, SaaS logging, and identity protections around collaboration platforms before layering AI agents on top of them.

The article outlines common internet-facing exposures in 2026—such as exposed admin panels, brute‑forceable interfaces, credential reuse, and memory-scraping vulnerabilities like the described "MongoBleed" bug—that dramatically reduce time-to-exploit once a new flaw is disclosed.[2][3] It emphasizes that anything public-facing, including SaaS consoles and cloud management planes, becomes immediately high risk when such vulnerabilities appear.[2] From a RealGround perspective, these patterns map directly onto SaaS- and cloud-backed AI agents and platforms, whose admin panels, APIs, and data stores can be similarly exposed if not rigorously hardened and continuously tested. Organizations should subject their AI and SaaS control surfaces to continuous AI-focused red teaming to discover exposed endpoints, misconfigurations, and weak authentication flows before attackers do.

According to reporting, DragonForce ransomware operators deployed a new Go-based backdoor (Backdoor.Turn) that abuses legitimate Microsoft Teams TURN relay servers to disguise command-and-control traffic, making it appear as normal collaboration traffic and evading traditional network defenses.[1][3][6] The campaign shows long-term, covert persistence within a major U.S. services firm, without any evidence that Microsoft’s core infrastructure was breached; instead, standard Teams relay functionality was repurposed for malicious use.[1][3][6] For RealGround, this highlights that AI-enabled SaaS collaboration platforms and their networking primitives (e.g., TURN/QUIC over UDP 443) can be leveraged as covert channels for agent C2, requiring agents and defenses to treat "trusted" SaaS traffic as potentially hostile and to instrument process-aware and protocol-aware monitoring around these dependencies. Organizations should harden AI and agent architectures that rely on SaaS platforms by baselining expected service use, applying continuous red teaming against SaaS-based C2 patterns, and including SaaS communication behaviors in AI security readiness and threat modeling.

The article promotes a webinar on how modern breaches bypass MFA and evade traditional detection controls, emphasizing that legacy MFA mechanisms alone are no longer sufficient for robust identity security.[6][7] It indicates that attackers increasingly use advanced techniques to slip past conventional monitoring and authentication protections.[1][2][3] For AI-driven and SaaS-based systems that often rely on MFA-gated access and behavioral analytics, these same bypass methods can undermine assumptions about trusted sessions and authenticated identities, raising the risk of unauthorized access to AI agents, models, and connected data. RealGround analysis: organizations should apply continuous red teaming and adversary simulation against their AI and SaaS identity stacks (including MFA, session management, and detection logic) to validate that AI-access paths remain protected even when attackers successfully bypass legacy MFA and traditional monitoring.

According to Google, the China-linked espionage group UNC6508 compromised REDCap research servers at North American medical, academic, and military research organizations, harvesting credentials and then using legitimate Google Workspace content compliance rules to silently BCC sensitive research and defense-related emails to attacker-controlled Gmail accounts.[1][2] The operation persisted for over a year and relied on abusing built-in cloud admin features (mail rules) rather than deploying additional malware, making it difficult to detect.[1][2] From a RealGround perspective, any AI-enabled workflows or research pipelines built on top of SaaS platforms like Google Workspace inherit this risk: if an attacker gains admin access, they can rewire rules, data flows, or integrations used by AI agents to exfiltrate training data, prompts, or model outputs without changing the AI code itself. Organizations should use an AI Security Readiness Assessment to map AI-related data flows in SaaS environments, enforce phishing-resistant MFA and least-privilege admin controls, and regularly audit mail rules, automation, and third-party integrations that AI agents depend on for potential covert ex

The article reports a critical vulnerability in Splunk Enterprise (CVE-2026-20253, CVSS 9.8) that allows an unauthenticated, network-reachable attacker to create or truncate arbitrary files via a PostgreSQL sidecar service endpoint lacking authentication in versions below 10.2.4 and 10.0.7.[1][3] Splunk’s advisory confirms that this flaw can be exploited remotely without credentials, potentially leading to full system compromise, data destruction, or staging of malicious code, and recommends upgrading to fixed versions such as 10.4.0, 10.2.4, or 10.0.7.[1][3][5] From a RealGround perspective, any AI agents or analytics pipelines that rely on Splunk as a logging, telemetry, or decision backend face elevated SaaS AI risk: successful exploitation could tamper with logs used for model monitoring, hide or fabricate security signals, and indirectly mislead AI-driven detection or response workflows. Organizations should treat Splunk as part of their AI attack surface, rapidly patch affected instances, harden network exposure, and include Splunk configuration, access control, and log integrity checks in their AI Security Readiness Assessment.

The article reports that the ShinyHunters extortion group exploited a zero‑day vulnerability (CVE-2026-35273) in Oracle PeopleSoft to compromise more than 100 organizations, with universities disproportionately affected, stealing large volumes of sensitive student and administrative data and issuing extortion demands.[1][2][3] Oracle reportedly released an advisory only after the active exploitation window, indicating a period of unpatched exposure. From a RealGround perspective, this highlights a critical SaaS and software supply‑chain risk: AI systems and agents that integrate with or depend on ERP/SIS platforms like PeopleSoft may silently inherit compromise, data integrity issues, and unauthorized data exposure when core university business systems are breached. Organizations should treat major SaaS/ERP platforms as part of their AI supply chain, maintain SBOM and dependency visibility, and ensure that AI agents have least‑privilege, monitored access so that a PeopleSoft‑level breach cannot be used to pivot into AI workflows or exfiltrate training and inference data.

The article reports on GreatXML, a newly disclosed Windows BitLocker bypass where a crafted unattend.xml and modified Recovery directory placed on the recovery partition can, after Windows Defender Offline Scan has been used at least once, spawn a SYSTEM shell in WinRE with unrestricted access to BitLocker-encrypted volumes, without needing the password or key.[1][3][4][5] This is a local physical-access zero-day tied to Microsoft Defender Offline Scan and weak validation of configuration files in the Windows Recovery Environment, and full public proof-of-concept code has already been released.[4][5] From a RealGround perspective, while this is not an AI-model exploit, it materially increases endpoint compromise risk; any AI SaaS or agents whose secrets, tokens, or models are stored on affected Windows endpoints are more exposed to data theft and lateral movement if GreatXML is used. Organizations should harden BitLocker (e.g., TPM+PIN), restrict physical access, and include WinRE/BitLocker bypass scenarios in their AI security readiness planning to protect AI-related credentials, training data, and local model artifacts.

The article reports that security teams are increasingly overwhelmed by high volumes of alerts, driving adoption of AI, automation, and richer context to filter real threats from noise.[1][4][9] It frames alert fatigue itself as a security risk because missed or delayed responses to true incidents become more likely as human capacity is exceeded.[3][4] From a RealGround perspective, as SOCs embed AI/ML-driven triage and automation—often delivered as SaaS platforms—these systems become critical security controls whose failure modes (misclassification, over-filtering, or over-trusting vendor logic) can introduce SaaS AI risk, including undetected attacks and opaque decision pathways. Organizations should treat AI-based alerting and triage as high-value SaaS AI components, harden their configurations, and continuously red-team and monitor them so that attempts to exploit or bypass AI-driven filters are detected before they create systemic blind spots.

The article reports that Aryon Security, a Tel Aviv-based cloud security startup, raised a $29M Series A round to expand its Cloud Security Enforcement Platform, which prevents risky cloud configurations and misconfigurations before deployment to production environments.[2][3][4] The platform uses AI-powered, policy-based scanning and integrates into organizations' existing DevSecOps and cloud stacks, enforcing customer-defined security controls across environments.[3][4][5] From a RealGround perspective, this type of AI-enabled SaaS security control becomes part of an organization's AI and software supply chain: security teams must evaluate how its AI-driven policy logic is trained, how customer configurations and cloud metadata are protected, and what transparency (e.g., SBOM, model/documentation) exists to manage dependencies and reduce vendor-introduced risk. Organizations should treat Aryon-like platforms as critical third-party AI/SaaS components, applying rigorous supply chain, data handling, and configuration governance reviews before and during adoption.

The article reports that Cyera, an AI-driven data security/SaaS provider, has raised hundreds of millions of dollars at a multi‑billion‑dollar valuation, with total funding now exceeding $2 billion, making it one of the most valuable private cybersecurity firms.[1][2][4][5] This capital surge signals rapid customer adoption and likely expansion of its AI-powered data discovery and classification capabilities across IaaS, SaaS, DBaaS, and on‑prem environments.[5] From a RealGround perspective, the growing dominance of an AI-native data security SaaS platform concentrates data protection, telemetry, and potentially sensitive metadata about enterprise environments into a single external provider, increasing SaaS AI risk and supply-chain exposure. Organizations integrating Cyera-like platforms should undergo structured AI security readiness assessments, require SBOM-level transparency for AI components, and implement CISO-level governance for data flows, model behavior, and third-party AI dependencies.

The article reports that ServiceNow experienced a security incident where unknown threat actors exploited a flaw to gain deeper, unauthorized access to certain customer instances, prompting the company to deploy a security update to hosted environments on June 5, 2026. This is a factual disclosure of a SaaS platform vulnerability and active exploitation impacting customer data and workflows. From a RealGround perspective, this highlights SaaS AI risk in the application and data layer that AI agents may depend on, since compromised ServiceNow instances could be used to feed poisoned data into AI workflows or expose sensitive tickets and knowledge bases to downstream AI systems. Organizations should treat core SaaS platforms like ServiceNow as part of their AI supply chain, validating access controls, hardening integrations, and performing continuous red teaming of AI agents that rely on data or actions originating from such SaaS systems.

The article reports that ServiceNow patched a vulnerability affecting hosted customer instances, which had reportedly been known internally since April 7 and was exploited against some customers. ServiceNow applied updates to customer environments to remediate the flaw, similar to prior cases where the company rapidly patched critical ServiceNow platform vulnerabilities across hosted, partner, and self-hosted instances.[1][6] From a RealGround perspective, this illustrates SaaS AI risk and broader SaaS platform supply-chain exposure: when a core platform service used to host AI-driven workflows has a latent, exploited vulnerability, all dependent AI automations and data flows inherit that risk. Organizations should treat ServiceNow and similar platforms as critical AI/SaaS supply-chain components, demand timely vulnerability visibility, and maintain third-party risk programs that track SaaS patches, exposure windows, and potential blast radius across integrated AI agents and workflows.

The article argues that the main security risk in modern networks is no longer lack of detection or tooling, but the fragmented, manual work that occurs *between* tools, creating gaps between alerting and execution that extend outages and slow incident response.[1] It promotes "intelligent workflows" to orchestrate and automate actions across an organization's expanding tech stack, effectively turning multiple security/SaaS systems into a more unified, automated environment.[1][3] From a RealGround perspective, any orchestration layer or intelligent workflow that coordinates security tools—especially if AI-driven—becomes a high‑value SaaS and automation control point whose misconfiguration, abuse, or compromise can magnify impact across all integrated systems. Organizations using such intelligent workflows should treat them as critical SaaS/AI agents, applying secure agent design, least-privilege integrations, and rigorous change and runbook controls to prevent automation from becoming a systemic failure point.

The article reports that Veeam patched a critical remote code execution vulnerability (CVE-2026-44963, CVSS 9.4) in its Backup & Replication software that allows any authenticated domain user to execute arbitrary code on domain-joined backup servers.[1][7] This affects version 12 builds prior to 12.3.2.4854, while version 13.x is not impacted due to architectural changes.[1][8] From a RealGround perspective, compromise of a backup platform that may store AI system snapshots, model binaries, vector databases, or configuration secrets is a significant AI supply-chain and resilience risk: an attacker gaining RCE on the backup server can tamper with AI models, training data backups, or agent configs and then restore these malicious states as "trusted" versions. Organizations should integrate this class of backup RCE into their AI SBOM and supply-chain threat model, enforce rapid patching for infrastructure supporting AI workloads, and apply strong network segmentation, least-privilege domain access, and integrity checks on restored AI-related backups.

The article describes OpenAI’s cookbook guidance for building AI agents that can safely use tools, handle data, and operate inside workflows that may touch sensitive systems, including SaaS and fintech environments.[4][6] It emphasizes configuration patterns, guardrails, and design choices to reduce misuse paths and control how agents act when given access to external tools or data sources.[4][6] From a RealGround perspective, this is directly relevant to SaaS AI risk because misconfigured agents integrated with SaaS or internal APIs can lead to data leakage, over-privileged tool access, and exploitable business logic. Organizations should pair these practices with a Secure AI Agent Build process, targeted AI Agent Business Logic Audits, and Continuous AI Red Teaming to validate that real-world attacks (e.g., prompt injection, unsafe tool use, or privilege escalation via agents) are prevented before and after deployment.

The article reports an actively exploited, unpatched zero-day (CVE-2026-20245) in Cisco Catalyst SD-WAN Manager that allows an authenticated local attacker with netadmin privileges to upload a crafted file and execute arbitrary commands as root due to insufficient input validation in the CLI.[1][2][5] Cisco notes there are no workarounds, it affects all SD-WAN deployment types (on‑prem, Cloud-Pro, Cisco-managed cloud, and Government/FedRAMP), and exploitation has in some cases resulted in malicious configuration changes being pushed to edge devices.[1][2][5] From a RealGround perspective, any AI-enabled or AI-orchestrated SaaS or network management stack that depends on this SD-WAN fabric inherits supply-chain and control-plane risk: a compromised SD-WAN Manager can sabotage traffic to or from AI services, exfiltrate model and data flows, or be used as a stable foothold for lateral movement into AI infrastructure. Organizations should treat SD-WAN controllers as high-value components in their AI architecture and prioritize hardening, access minimization, continuous compromise assessment, and red teaming of management planes that underpin AI workloads.

SecurityWeek reports that threat actors are actively exploiting critical vulnerabilities in the Kirki and Burst Statistics WordPress plugins to perform unauthenticated privilege escalation, reset admin passwords, and ultimately take over websites.[1] These bugs (including CVE-2026-8206 and CVE-2026-8181) allow attackers to hijack administrator accounts and abuse REST API functionality, with hundreds of thousands of sites potentially exposed if not patched.[1][2][3] From a RealGround perspective, any AI-enabled services or plugins integrated into a compromised WordPress instance (for example, AI chat widgets, content-generation agents, or API keys stored in the CMS) could be indirectly exposed, allowing attackers to exfiltrate secrets, tamper with AI workflows, or use the compromised site as an entry point into broader SaaS or AI infrastructure. Organizations should treat CMS plugin security as part of their SaaS AI risk surface, ensuring rigorous patching, access control, and an AI Security Readiness Assessment to map and harden all AI-related integrations that rely on or trust web applications like WordPress.

The article reports that Coralogix, a full-stack observability provider, raised $200M at a $1.6B valuation to scale its unified platform for logs, metrics, traces, security, and AI observability. This indicates growing enterprise dependence on a third-party SaaS platform for monitoring and securing AI-driven systems. From a RealGround perspective, this concentration of telemetry and AI observability data in a single SaaS provider increases exposure to data leakage, supply chain compromise, and configuration/permission mismanagement risks. Organizations adopting such platforms should assess SaaS security posture, vendor SBOM and supply chain hygiene, and implement strong governance around what AI and security data is exported to, processed by, and retained in the observability service.

The article reports that password manager Dashlane experienced a brute-force attack in which an external threat actor targeted user accounts and successfully downloaded the encrypted vaults of fewer than 20 personal-plan users before protections locked accounts.[1][2] Dashlane states that the vaults remain encrypted and that two-factor authentication was under attack as part of the attempt to gain access.[1][2] From a RealGround perspective, this highlights SaaS risk patterns that are directly applicable to AI-powered SaaS products, where user credentials, 2FA implementations, and encryption models are central to protecting sensitive data and model-connected resources. Organizations running AI SaaS or integrating password/secret managers into AI workflows should regularly assess authentication hardening, rate limiting, anomaly detection, and incident response around user accounts and stored secrets using an AI Security Readiness Assessment.

According to Dashlane and media reporting, some user accounts on the Dashlane password manager platform were targeted by a brute-force attack, triggering Dashlane’s automated defenses that locked or suspended a subset of accounts and prevented large-scale compromise of vault data.[3][5] The article indicates that only limited encrypted vault data was downloaded in connection with the attack, and Dashlane reports no evidence of broader system compromise.[3][5] From a RealGround perspective, this illustrates how consumer SaaS security controls (rate limiting, account lockout, anomaly detection) are critical patterns that should also be applied to AI-powered SaaS products, especially where they protect sensitive data such as API keys, credentials, or proprietary prompts. Organizations deploying AI SaaS should ensure similar brute-force protections, strong authentication, and monitoring are in place and periodically validated through an AI Security Readiness Assessment.

The Kaseya article explains that AI is amplifying existing SaaS security risks by driving signal overload, SaaS sprawl, and more sophisticated identity-based attacks, and recommends unifying telemetry across identity, SaaS, endpoints, and cloud systems, plus automation and correlation of signals to improve security operations.[1] It frames AI as a force-multiplier for attackers and defenders, emphasizing identity-centric architectures and automated response rather than any specific model flaw or CVE-like vulnerability.[1] From a RealGround perspective, this reflects a systemic SaaS AI risk: organizations increasingly depend on AI-enhanced security tooling and AI-driven workflows, which require readiness assessments and CISO-level guidance to ensure identity, logging, and automation are governed and architected securely across SaaS environments. Practically, security teams should evaluate how AI-enabled telemetry correlation and automated response are designed, tested, and red-teamed to prevent misconfigurations, over-privileged identities, or automation errors from becoming high-impact SaaS breaches.

Sage reports that small and medium-sized businesses are rapidly adopting AI, which is increasing cybersecurity pressure and revealing gaps between stated cybersecurity priorities and the practical resilience of their operations.[1] The press release frames these AI-driven resilience gaps as a core business risk for SMBs rather than a purely technical concern.[1] From a RealGround perspective, this indicates that many SaaS-dependent SMBs are deploying or consuming AI-enabled services without systematically assessing AI-specific threats such as data exposure, model misuse paths, and supply-chain dependencies. An AI Security Readiness Assessment can help these organizations map their AI usage, identify control gaps in SaaS and AI workflows, and prioritize pragmatic security improvements aligned with business resilience goals.

The article identifies five AI-related SaaS threats—Shadow AI, data privacy risks from AI training, evolving SaaS terms enabling broader data use, vulnerabilities in AI data storage, and third-party data sharing—as operational risks to organizations using AI inside SaaS environments.[1] It emphasizes that unsanctioned AI usage and opaque vendor practices can expose sensitive business data, extend the attack surface, and complicate compliance.[1] From a RealGround perspective, these issues map to a broader SaaS AI risk posture problem: organizations need structured discovery of AI use in SaaS, governance over what data AI can access or train on, and continuous assessment of AI-linked SaaS and third-party supply chain. Practically, security leaders should prioritize an AI-focused readiness assessment and SaaS AI supply chain review, then embed AI-specific policies and executive advisory to manage ongoing risk.

The LastPass article frames Shadow AI as a SaaS-centric risk where unsanctioned and embedded AI features inside SaaS apps create unmanaged identity paths, weak or missing MFA, reused credentials, and persistent agent/integration access that security teams do not see.[5] It links these gaps to increased exposure of sensitive and regulated data as employees and automated agents interact with AI inside SaaS environments without proper governance, identity controls, or monitoring.[5][2] From a RealGround perspective, this is best classified as a SaaS AI risk because the core issue is AI functionality embedded in or attached to SaaS expanding the identity and access surface (OAuth tokens, agents, integrations) rather than model-level attacks. Practically, this implies organizations should inventory AI-enabled SaaS, tighten identity and access controls (including MFA and OAuth scopes), and formalize AI usage and governance baselines through an AI Security Readiness Assessment.

The article describes how employees are using generative AI to 'vibe code' full applications, wiring them directly into production systems and exposing them on the public internet without Security or IT involvement.[5] This shifts 'shadow AI' from ad hoc prompt use to unsanctioned SaaS-like applications that interact with live data and internal services, creating a large, largely invisible attack surface. From a security perspective, this raises significant SaaS AI risk: unreviewed code, missing authN/Z, insecure integrations, and lack of monitoring can lead to data leakage and compromise of core systems. RealGround would recommend an AI Security Readiness Assessment and policy support to inventory and govern shadow AI apps, combined with Secure AI Agent Build patterns to give teams safe, approved ways to create AI-powered applications.

The article reports that French cybersecurity startup MokN raised $15 million in Series A funding to expand its 'phish-back' platform, which uses ultra-realistic decoy access points (such as fake VPN or webmail portals) to lure attackers, capture compromised credentials, and trigger automated recovery workflows before those credentials are abused.[1][3] This represents an active identity recovery approach to credential-theft defense, positioning MokN as part of modern SaaS-based security tooling that integrates into enterprise environments and existing security stacks.[1][3] From a RealGround perspective, while the article does not explicitly mention AI, platforms of this type increasingly embed machine learning for anomaly detection, automation, and decisioning, which introduces SaaS AI risk around opaque logic, potential misclassification, and dependency on a third-party SaaS provider for critical identity protections. Organizations adopting such a service should evaluate its AI/automation components, data flows, and integration touchpoints as part of an AI Security Readiness Assessment, assess vendor and supply-chain exposure (e.g., SBOM, model dependencies), and use Continuous

The article describes active exploitation of CVE-2026-35616, a critical unauthenticated access-control bypass in FortiClient EMS that allows threat actors to hijack trusted management APIs and push a credential-stealing payload (EKZ Infostealer) to all managed endpoints via PowerShell and fake Fortinet update binaries.[1][2][4] Attackers use the EMS control plane and features such as VPN on_connect scripts to distribute malware that harvests browser passwords, cookies, and autofill data, then exfiltrates it over HTTP to attacker infrastructure.[1][2][4] From a RealGround perspective, this highlights how compromise of a centralized management/SaaS-like control plane in an AI or IT environment (e.g., an AI platform’s orchestration or agent-management service) can turn otherwise trusted update and scripting channels into large-scale malware or data exfiltration vectors. Organizations deploying AI platforms should treat management/control planes as part of their AI supply chain, maintain an SBOM and vulnerability tracking for these components, and strictly limit network access and script-execution features to reduce the blast radius of similar abuse.

The article reports a critical, unpatched remote code execution vulnerability in Gogs, a self-hosted Git service, that allows any authenticated user to execute arbitrary code by abusing a malicious branch name during a 'Rebase before merging' operation, with a CVSS score of 9.4 and no CVE assigned.[1] Successful exploitation lets attackers fully compromise the Gogs server, access all repositories, dump credentials, move laterally, and read private, cross-tenant repositories, with over a thousand internet-facing instances identified and a Metasploit module publicly available.[1] From a RealGround perspective, any AI development or MLOps pipelines that rely on Gogs as a code or model repository face elevated supply chain risk: an attacker with low-privilege access could tamper with application code, AI agents, or model artifacts, silently poisoning builds or inserting backdoors. Organizations should treat Gogs as a critical component in the AI software supply chain, implement strong network isolation and account controls, and include Gogs instances in SBOM-driven monitoring and continuous vulnerability management until an official patch is available.

According to IDC research reported by ERP News, over 80% of SMBs are either unprepared or only in the early stages of readiness for AI-related cyber threats, even as they rapidly adopt AI, SaaS, and third‑party services.[2][4] The same research indicates that nearly a quarter of SMBs have not implemented any dedicated protections for AI applications, leaving them exposed to data leakage, insecure integrations, and AI-driven attack automation.[1][2] From a RealGround perspective, this reflects a systemic SaaS- and cloud-based AI risk posture problem, where externally hosted AI and ERP/SaaS tools are integrated without mature security governance, controls, or third‑party risk management. Practically, SMBs need structured AI security readiness assessments, CISO-level guidance, and formal AI policies to define data handling, integration security, and monitoring requirements for any AI or SaaS deployment before usage scales further.

The article argues that modern security operations centers (SOCs) must move beyond a 'fortress' mindset focused only on perimeter defenses and point detections, because real-world incidents often begin as low-visibility, routine-seeming activities that accumulate risk over time. It emphasizes earlier risk identification, continuous monitoring across identities and cloud/SaaS environments, and better scoping of blast radius to contain threats before they become full incidents. For AI-enabled SOC tooling and SaaS-based detection/orchestration platforms, this implies a need to harden data flows, access patterns, and automation logic so that AI-driven detections, playbooks, and enrichment services cannot be quietly abused or misled in those early, pre-incident phases (RealGround analysis). Organizations should assess and regularly test their AI-assisted SOC pipelines—especially those integrated with SaaS logging, EDR, and cloud telemetry—to ensure they do not introduce new blind spots, escalation paths, or data leakage channels as they try to 'shut down incident risks early' (RealGround analysis).

The article reports on CVE-2026-45659, a high-severity (CVSS 8.8) remote code execution vulnerability in Microsoft SharePoint Server caused by deserialization of untrusted data, which allows any authenticated user with minimal 'Site Member' permissions to execute arbitrary code over the network on affected SharePoint instances.[1][2][3] Microsoft has released patches for SharePoint Server Subscription Edition, 2019, and Enterprise 2016, and while exploitation is currently assessed as less likely with no public PoC, unpatched servers remain at significant risk of full compromise.[1][2][3] From a RealGround perspective, AI-enabled workflows and agents that integrate with on-prem or self-hosted SharePoint for data access or orchestration could be indirectly exposed if a compromised SharePoint server is leveraged to pivot into AI infrastructure, exfiltrate training/operational data, or tamper with documents and prompts consumed by AI systems. Organizations should ensure SharePoint patching is tightly integrated into their broader AI security readiness and asset management, especially where SharePoint is a data source or control surface for AI agents and decision-support systems.

The article is about Network Detection and Response (NDR) systems that include agentic AI capabilities, which teams use to catch threats earlier, triage faster, and reduce false positives. It does not describe a confirmed AI attack or exploit; rather, it discusses operational benefits and the persistence of “noisy” reputations in NDR. RealGround should treat this as a low-severity SaaS/AI operations topic, with the main security implication being the need to validate governance, alert quality, and human oversight before deploying agentic automation.

The article reports active exploitation of CVE-2026-26980, a critical unauthenticated SQL injection flaw in Ghost CMS (CVSS 9.4) that allows attackers to read arbitrary database data, steal Admin API keys, and bulk-inject malicious JavaScript into pages, driving large-scale ClickFix/fake CAPTCHA malware campaigns across 700+ sites in sectors including AI/SaaS and fintech.[1][5] The vulnerability, fixed in Ghost 6.19.1, is still being abused against unpatched instances to hijack content and weaponize trusted sites as malware delivery platforms.[1][5] From a RealGround perspective, this highlights SaaS and CMS platforms as critical parts of the AI application supply chain: compromise of a CMS that hosts AI product blogs, documentation, or embedded agents can be used to deliver malicious scripts to users or operators and to poison content that downstream AI agents consume. Organizations should treat CMS platforms as high-trust supply-chain components, enforce rapid patching and key rotation, and incorporate Ghost and similar services into SBOM-driven dependency tracking and security monitoring to prevent content-layer compromise from cascading into AI workflows and user endpoints.

The article reports that SMBs are increasing cybersecurity investment as AI adoption, SaaS expansion, and third‑party cloud tools significantly broaden their attack surface, especially through integrations and external services.[7] It also notes that many small firms lack formal AI security controls or governance, leaving them exposed to misconfigured SaaS apps, compromised connectors, and data leakage from staff use of AI tools.[7] From a RealGround perspective, this reflects a concentrated SaaS AI risk pattern where unmanaged third‑party apps and AI features can exfiltrate sensitive data or create hidden dependencies without proper oversight. Practically, SMBs should prioritize an AI Security Readiness Assessment to inventory AI/SaaS use, map data flows, and define governance and technical controls for third‑party and cloud-based AI integrations.

The article reports a critical CVE-2026-20223 vulnerability (CVSS 10.0) in Cisco Secure Workload’s internal REST APIs that allows an unauthenticated remote attacker to send crafted API requests to read sensitive data and modify configurations across tenant boundaries with Site Admin privileges on both SaaS and on‑prem deployments.[1][2][3][5] Cisco states there are no workarounds and customers must upgrade to fixed versions (3.10.8.3 or 4.0.3.17, or migrate from 3.9 and earlier) and that the flaw was found internally with no evidence of active exploitation yet.[1][2][3][5] From a RealGround perspective, any AI or data-processing agents integrated with Secure Workload APIs (for observability, policy automation, or remediation workflows) could be abused as a powerful data exfiltration and cross-tenant configuration channel if the underlying platform APIs are compromised, so organizations should: (1) rapidly patch or migrate, (2) restrict and monitor AI/automation access to high-privilege infrastructure APIs, and (3) include similar API-level privilege-bypass scenarios in continuous AI red teaming and supply-chain risk assessments.

The article reports that CISA added a critical Langflow vulnerability (CVE-2025-34291, CVSS 9.4) and a Trend Micro Apex One on‑premise flaw (CVE-2026-34926) to its Known Exploited Vulnerabilities catalog due to confirmed active exploitation.[1][2] For Langflow—an AI/LLM workflow and orchestration tool—the issue is an origin validation error combined with overly permissive CORS, missing CSRF protection, and a code-execution endpoint, enabling remote code execution, full system compromise, and exposure of stored access tokens and API keys, risking cascading compromise across integrated cloud and SaaS services.[1][2] Ctrl-Alt-Intel and Obsidian Security have documented exploitation of the Langflow bug by the MuddyWater Iran‑nexus APT group for initial access.[1][2] From a RealGround perspective, this represents a high-severity SaaS AI risk because compromising Langflow as an AI orchestration layer can pivot attackers into downstream LLM tools, vector stores, SaaS APIs, and other integrated services, turning one RCE into multi-platform credential theft and data exposure. Organizations should harden AI workflow platforms like Langflow with strict origin controls, CSRF protections, l

The article reports that small businesses are increasingly adopting AI-powered, largely autonomous cybersecurity tools delivered as cloud and SaaS services for threat detection, phishing protection, and compliance reporting, often without in‑house security expertise or formal AI risk management frameworks.[1] It also notes that these SMBs are attractive targets because of limited defenses and reliance on externally managed platforms for day‑to‑day operations and data protection.[1] From a RealGround perspective, this concentration of security functions in third‑party AI/SaaS tools creates SaaS AI risk around data access, configuration mistakes, vendor compromise, and unclear shared-responsibility boundaries. Implementing an AI Security Readiness Assessment and AI Policy Generator & Support can help SMBs formally define data handling rules, evaluate SaaS AI vendors, and put compensating controls around cloud AI tools that are operating without dedicated security staff.

The article describes a 0-click exploit chain on Pixel 9 that abuses vulnerabilities in the Dolby UDC audio codec, which is exposed because Google Messages performs automatic AI-powered transcription and searchability on incoming audio messages before user interaction.[4][1] This design makes audio decoders part of the 0-click attack surface across many Android devices, and the authors also highlight slow patch timelines and ecosystem-wide process gaps.[4][1] From a RealGround perspective, this is an example of AI-enhanced messaging and transcription features expanding remote attack surface and privilege boundaries in a SaaS-like communication stack, without sufficient threat modeling and hardening of the underlying media/ML pipelines. Organizations deploying similar on-device or cloud-based transcription/search services should perform structured AI Security Readiness Assessments to map new AI-driven data flows, minimize pre-interaction processing, harden codec and model runtimes, and establish faster coordinated patch and rollout processes for AI-exposed components.

The article profiles ten early-stage AI security vendors focused on AI-native exposure management, identity security for human and AI identities, verification of human–AI and agent–AI interactions, and fine-grained authorization for AI workloads across infrastructure, apps, data, and agents.[1] It highlights capabilities such as governing AI workloads, monitoring and controlling agentic AI behavior, eliminating shadow AI, and enforcing real-time policies on AI-agent-to-data and agent-to-agent interactions, which are directly relevant to SaaS and startup environments adopting LLMs and AI agents.[1] From a RealGround perspective, this underscores that SaaS teams deploying LLMs and agentic workflows face material risks around unauthorized data access, over-permissioned agents, and opaque AI interactions, and therefore benefit from structured readiness assessments, secure agent design, and explicit AI usage and access policies aligned to these new control layers. Practically, organizations should map their current and planned AI agents, define least-privilege and verification controls for agent actions and data access, and integrate continuous monitoring and governance for AI interacti

The Crunchbase article reports that several new unicorns in April 2025 operate in security, data, and healthcare and increasingly rely on AI to deliver privacy, security, and infrastructure protection capabilities.[1] These companies provide tools such as AI-enhanced data loss prevention, secure connectivity, and defense-oriented platforms as demand grows for protecting sensitive data and critical infrastructure in AI-enabled environments.[1] From a RealGround perspective, this trend indicates that many rapidly scaling SaaS and infrastructure providers are embedding AI deeply into their products and operations, which introduces risks around data handling, model behavior, and access control at scale. Organizations adopting these AI-driven security and healthcare tools should assess vendor AI security posture, validate data protection and governance controls, and ensure their own architectures and policies are ready to integrate AI-heavy SaaS securely.

The article defines criteria for the 'Best SaaS Product for Cybersecurity' award, requiring strong threat detection, vulnerability management, IAM, compliance automation, security analytics, and real-time monitoring capabilities for SaaS platforms.[1][3] It is not AI-specific but explicitly applies to SaaS solutions, including those that may embed AI or LLM features, and stresses integration with existing controls and robust protection of sensitive data.[1] From a RealGround perspective, these criteria map directly to SaaS AI risk: any SaaS product that incorporates AI or agents must ensure that AI features inherit and do not weaken core controls for identity, data protection, monitoring, and compliance. Practically, organizations should use an AI Security Readiness Assessment and AI CISO Advisory to benchmark AI-enabled SaaS against these expectations, and apply Secure AI Agent Build practices so LLM features align with established SaaS security and compliance baselines.

According to CrowdStrike’s 2024 Threat Hunting Report, nation-state and eCrime actors are increasingly exploiting legitimate credentials and identities to pose as insiders, bypass legacy controls, and conduct hands-on-keyboard intrusions, including a 55% increase overall and a 75% increase in healthcare, while also targeting cloud control planes for lateral movement and data theft.[1][2][3][4] These findings highlight a growing trend of identity-based attacks across cloud environments, where valid credentials and misused remote tools enable stealthy cross-domain intrusions that leave minimal forensic footprints.[1][2][3][4] From a RealGround perspective, AI SaaS, LLM-backed services, and agent frameworks that depend on cloud identities, access tokens, and control-plane APIs are directly exposed to these techniques, making identity hardening, token-scoped access, and continuous adversary-emulation of credential abuse critical to prevent AI agents from being hijacked or misused. Organizations should treat cloud and SaaS identity layers as primary attack surfaces for AI systems and implement secure agent architectures, proactive red teaming focused on identity abuse, and readiness

According to OpenAI’s incident reporting and third-party coverage, a distributed denial-of-service (DDoS) attack against OpenAI caused periodic outages and elevated error rates for ChatGPT and its API, disrupting availability for both end users and developers who integrate these services into their products.[1][2][6][8] The incident did not involve model or data compromise, but it demonstrated that major AI platforms are operational targets whose uptime can be materially affected by external attackers.[1][2] From a RealGround perspective, this fits a SaaS AI risk pattern: organizations that build agents, SaaS workflows, or critical business processes on commercial LLM APIs inherit those availability and resilience risks and must treat AI providers as key third-party dependencies in business continuity planning. Practical implications include stress-testing failover strategies, defining SLAs and RTO/RPO expectations with AI vendors, and incorporating AI-service outage scenarios into broader SaaS and supply-chain risk management.