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Frédéric R. shared this🔐 𝗦𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗔𝗜 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗰𝗮𝗻𝗻𝗼𝘁 𝘀𝗲𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 Every product team feels the same pressure these days: they need to ship AI, fast, everywhere. For most, that means feeding user data into a model and iterating. At Dashlane, we cannot do that. Our zero knowledge architecture means only the user sees their own data. Not hackers, not us, not our cloud provider. 🚫 So we had to rethink the entire AI lifecycle: how we train models, how we deploy, how we debug without ever seeing the inputs. Kaouther Ouenniche and I wrote up what we learned across two use cases 👇 🎣 Anti phishing detection Trained without touching user browsing data. Runs on device using around 80 lightweight features instead of a large model reading raw pages. 🛡️ Omnix AI Advisor Turns audit logs we can never see into security insights, using confidential computing and a Mistral model running inside Amazon Web Services (AWS) EC2 Attested Instances. Data gets decrypted, processed, and re encrypted entirely inside a hardware isolated enclave. Neither Dashlane nor AWS ever sees plaintext. 📋 Five takeaways from the full post: 1. Define your privacy requirements before you start coding 2. Build training data for coverage, not volume 3. Start with the smallest model that fits inside your privacy boundary 4. Match deployment to your constraints: on device when you can, confidential computing when you need cloud scale 5. Ship incrementally. You do not need a complete solution to start delivering value Privacy preserving AI is not just an aspiration. We are shipping it in production today. ✅ Full post 🔗 https://lnkd.in/g5_RCamGPrivacy-Preserving AI: Shipping AI When You Can't See the Data | DashlanePrivacy-Preserving AI: Shipping AI When You Can't See the Data | Dashlane
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Frédéric R. posted thisMost AI assistants processing sensitive data rely on you trusting a contract with the provider. At Dashlane, we just launched the beta for Omnix AI Advisor, a security assistant embedded in the Dashlane admin console. It lets IT and security admins query their organization's credential signals in plain language, from riskiest users to compromised passwords, without ever exposing that data. It leverages our own Confidential AI Engine, ensuring privacy at the architecture and hardware level. 🔒 𝗛𝗼𝘄 𝘄𝗲 𝘀𝗼𝗹𝘃𝗲𝗱 𝘁𝗵𝗲 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁 AI needs data in the clear to run inference. Our zero-knowledge architecture means we can never see or route that data through an external inference API, no matter the contractual protections. A contract tells you what a provider promises. It tells you nothing about what's technically possible for them to do. 🔑 The answer was confidential computing. We run a self-hosted open-weight model (Ministral 3 14B from Mistral AI, via llama.cpp) on GPU-accelerated EC2 attested instances on Amazon Web Services (AWS), hardened with NitroTPM attestation to replicate the isolation properties of a Nitro Enclave. No operator access, no persistent storage, no data leaving the enclave boundary. Before the orchestrator and the LLM exchange a single byte, they perform a mutual attestation, each side cryptographically proving what code it's running. That handshake is what makes the privacy guarantee structural. 🛡️ The full post covers the data flow, the attestation protocol, and how we built the secure channel. 👇 Thanks to Amazon Web Services (AWS) and Mistral AI for the collaboration that made this architecture possible. 🤝 #PrivacyPreservingAI #ConfidentialComputing #ZeroKnowledge #CredentialSecurity #AI https://lnkd.in/g-TgkeTb
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Frédéric R. shared thisWhat's new: Microsoft Sentinel® integration, real-time compromised password detection, and moreWhat's new: Microsoft Sentinel® integration, real-time compromised password detection, and moreDashlane
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Frédéric R. posted this𝗕𝘂𝗶𝗹𝗱 𝘃𝘀 𝗕𝘂𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗮𝗴𝗲 𝗼𝗳 𝗔𝗜 AI coding tools are changing what engineers, and others, can build. They are not changing whether they should. The build vs. buy debate has been a constant in IT and engineering for decades. The question was never "can we build it?" It was always "should we?" AI now lowers the barrier to writing code, but it does not lower the cost of owning it. Before reaching for an AI coding tool to roll your own solution, ask these questions: • Is this a one-time need, a throwaway script, or a short-lived internal tool? Build it. AI makes this faster and cheaper than ever, and that may be where you can get quick wins. • Is this a generic need? If the market already has mature solutions, you are not building a competitive advantage. You are reinventing the wheel, with extra maintenance and less innovation. • Do you have the team to own it? Writing code may be the easy part now. Testing it, securing it, monitoring it, updating dependencies, handling incidents at 2am: none of that changed. Humans are still going to carry the pager. • How does it fit your stack? Custom tools need to integrate with your identity provider, your logging pipeline, your access controls, your compliance requirements, your other tools. That integration has a significant cost in itself. • Who trains your users? Adoption does not happen because a tool exists. Internal tooling needs documentation, support, and change management, the same as any SaaS product you would deploy. AI also amplifies a risk that already existed: long-term maintenance and continuity. When the engineer who built the tool moves on, you inherit a codebase that AI may have helped write but no one fully owns. That is a risk that already existed but will be reinforced for organizations. AI coding tools may reduce the cost of the initial build, but they don’t reduce the cost of the total lifecycle. One dimension that applies regardless of your choice is security. Whether you build or buy, every tool in your stack needs to be secured. The risk profile is different, though. With a vendor product, you inherit their security posture, their vulnerability management cadence, and their incident response, as well as their exposure and risk profile. With a tool built in house, you own all of that, including code audits, dependency patching, access controls, and breach response. AI-generated code is not inherently secure code. My advice: build when the need is unique or core to your competitive advantage, and to keep buying when the market has already solved the problem and offers solutions that provide the right ROI for you. How is your team rethinking build vs. buy in the AI era?
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Frédéric R. posted thisA growing number of enterprises are reportedly blowing through their AI budgets due to token costs. This has largely been a consequence of a hasty push towards tokenmaxxing, the practice of using as many AI tokens as possible as a proof that you are an AI power user. You can achieve that by packing as much context as possible into AI prompts and generating the longest possible outputs. Some companies even have leaderboards tracking this! The assumption is that more tokens produce better results, but I think it is the wrong assumption. A farmer who over-fertilizes does not grow a bigger crop. A database query that scans every row in the table is not the most powerful query. It is the most expensive one. The same logic applies here. Throwing tokens at a problem is not a strategy. It is a symptom of not understanding the tool or trying to game unhealthy incentives in your company. For coding agents, this is especially visible. Teams that are stuffing entire codebases into context, adding redundant instructions, using the most expensive models, are not getting better output. They are paying more for worse results and will have to spend engineering time cleaning it up after. The right question is not how many tokens a task can absorb. It is how few tokens are needed to complete it well. AI tools, like any tool, require precision. The right context for the right task. An agent that completes a focused task with 2k tokens and high accuracy is worth more than one that burns 200k tokens to produce the same output. This matters for another reason: ROI conversations around AI spend are starting to surface at the leadership level. AI spend is accelerating across every engineering org, and the question of what that spend is actually producing will get asked. Teams that built discipline early, that measure cost per outcome rather than tokens consumed, will have a much easier time answering that question. Prompt engineering is still engineering. Apply the same discipline you would to anything else.
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Frédéric R. shared thisLast month we hosted our second Dashlane Security Happy Hour in New York. Umesh Shankar from Microsoft AI walked us through what a real trust framework for AI agents looks like, with concrete criteria around competence, judgement, and progressive authority. The framing that stuck with me was that we already apply nuanced trust to people, but we just haven't built the same concepts in how we approach AI systems yet. Rajan Kapoor from Material Security did a live teardown of an OAuth phishing stack he built in two hours for $5, with free-tier cloud infrastructure, like Cloudflare and Google. This means there is nothing to block because it all runs on infrastructure we already trust. And resetting the victim's password has no effect on OAuth tokens. That demo alone was worth the evening. Thank you to Umesh and Rajan for the generosity and clarity of their talks, and to everyone who joined us in person and on Zoom. I've written up both sessions with key takeaways here: https://lnkd.in/eTduX-fT And if you want to join our next event: we're hosting a Security Happy Hour as part of TECH WEEK by a16z on June 2. Details and registration in comments. #Cybersecurity #IdentitySecurity #AIAgents #SecurityLeaders #NYC #NYTechWeekNYC Security Happy Hour 2026: What a $5 Phishing Stack and AI Trust Framework Have in Common | DashlaneNYC Security Happy Hour 2026: What a $5 Phishing Stack and AI Trust Framework Have in Common | Dashlane
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Frédéric R. shared thisGrateful for the opportunity to speak at @UXDX USA this week in New York. My talk, "AI Without Access: Shipping Intelligence When You Can't See User Data," covered something I think about a lot at Dashlane: how do you build AI that works well for users without ever exposing their data? Privacy by Design is even more important in a world of AI that relies on data. How can we all make sure we both leverage the potential of AI and the value it can bring our customers without jeopardizing their privacy and security? Product and engineering leaders should think hard about where AI fits in their stack and what they owe their users. Thanks to the UXDX team for the invitation and for a great event. Recording coming soon. #UXDX #ProductDevelopment #EngineeringLeadership #PrivacyByDesign #AI
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Frédéric R. shared thisAt Dashlane, engineers are no longer the only people producing code. A product manager can prototype a feature. A sales engineer can ship a customer-facing script. A support agent can automate their own workflow. AI makes this possible for everyone. The risk surface expands with that capability. People making engineering-level decisions with less engineering context. So we built an AI governance framework for the whole company, not just the engineering team. Four tiers, and one core principle: you generate it, you own it. Even if AI wrote every line. The tiers are based on two variables: • Who uses the output • What data it touches Who created the code is irrelevant. • T1 (Exploratory) → local only, no real data, minimal guardrails • T2 (Internal tool) → employees, any internal data, engineering review required • T3 (External/shared) → outside Dashlane or customer-facing, engineer sponsor required • T4 (Production) → full SSDLC, quality gates, the works Across all tiers: no secrets in prompts, no customer data in prompts, approved tools only. Here is our second post in our AI adoption series 👇 https://lnkd.in/eN2mWARU #AIGovernance #CyberSecurity #EngineeringLeadership #AIAdoptionBeyond the Engineering Team: How We Governed AI Coding for Everyone | DashlaneBeyond the Engineering Team: How We Governed AI Coding for Everyone | Dashlane
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Frédéric R. shared thisMost breaches don't start with a sophisticated attack. They start with a door someone forgot to close. In early 2025, Oracle's cloud identity infrastructure was breached through a legacy server running software last patched in 2014, still internet-facing, still holding live identity data for 140,000 enterprise tenants. Around the same time, attackers ran a large-scale payment card skimming operation against dozens of retailers by exploiting a deprecated Stripe API endpoint, one that had been retired from the product but never decommissioned. Two different companies, two different attack methods, one root cause: part of their attack surface was invisible to them, and not to the attacker. 🔍 Your attack surface is every point where an attacker can interact with your system: → APIs and public endpoints → Webhooks and third-party integrations → Admin panels and background jobs → Contractor credentials → Legacy systems your team stopped thinking about years ago It grows every time you ship a feature, add a vendor, or pull in a new dependency. It never shrinks on its own. 🎯 Threat modeling is what you do with that knowledge. Before a feature ships, your engineers should be asking: What does this open? Who can reach it? What happens if it's abused? At Dashlane, we build on an assumed breach model. We design for the scenario where any layer could already be compromised. Our zero-knowledge architecture aims at ensuring that even a full infrastructure breach gives an attacker no useful access to user vault data. 🔐 Security should be embedded into architecture from day one, not added as a layer afterward. This is the first post in a new series on the security principles and architectural decisions behind how we build Dashlane, and what any engineering team can take from them. 👉 https://lnkd.in/ewDtd53E #Cybersecurity #SecurityEngineering #ThreatModeling #IdentitySecurity #SecureByDesignHow Attackers Think: Understanding Your Attack Surface Before They Do | DashlaneHow Attackers Think: Understanding Your Attack Surface Before They Do | Dashlane
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Frédéric R. liked thisFrédéric R. liked thisLe dernier meetup Dev With AI à Paris avant le break estival aura lieu chez Dashlane le 7 juillet 😎 Au programme : * Quentin B. - IA hors de contrôle ? Le guide pratique pour conteneuriser vos LLMs sans risque * Marwan Liani - Zero Knowledge, 100% IA : Révolutionner l'expérience utilisateur avec LLM sans sacrifier ses données * Adrien Cens - Développeur solo à l’ère de l’IA : produire à la vitesse d’une équipe Si vous ne pouvez pas venir ou si l'évènement est complet, vous pouvez suivre le livestream 🎥 Lien vers l'évènement en commentaire 👇
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Frédéric R. liked thisFrédéric R. liked this📢 Speaker Announcement 📢 The Cloud Native AI Summit Europe is landing in Paris on December 2-3, 2026! Our Call For Papers is officially live 👉 https://lnkd.in/eJCsenGU Every week, we share a glimpse of what to expect at the conference, starting with our confirmed speakers. Frédéric R., CTO @ Dashlane, will talk about “Deploying AI you can't see: patterns for privacy-preserving AI” 👉 https://lnkd.in/eRq9a_7c — Cloud Native AI Summit is a technical, independent and enterprise-focused conference contributing to the convergence of cloud-native foundations and AI in production. 🗓️ Timeline to Keep in Mind: 🔹 As of today: CFP is officially open 🔹 August 14th: CFP closes & talk reviews begin 🔹 September 24th: Official Agenda announcement 🔹 Oct 1st – Nov 20th: Talk review & Speaker coaching It’s now your turn to submit your abstract today and shape the inaugural Cloud Native AI Summit Europe 👉 https://lnkd.in/eJCsenGU #CloudNativeAi #Summit #Europe #platform #software #engineering #architecture #CloudNative #Tech #Conference #CFP #FOSTParis
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Frédéric R. liked thisFrédéric R. liked thisHier soir, mon train est resté bloqué jusqu'à 3h du matin : feu de forêt sur la voie. Ma voisine de derrière a passé 2 heures à démonter la SNCF. Mon voisin de côté s'est acharné sur le contrôleur, hilare et féroce à la fois. Moi au milieu, j'étais ébahie. Ébahie de voir à quel point nous avons oublié que nous dépendons de la nature qui nous entoure. Aucune technologie ferroviaire ne fait traverser une forêt en flammes à l'heure. La SNCF ne peut pas arrêter une sécheresse. Elle ne peut pas faire avancer un train quand les câbles ont fondu. Face au climat, elle ne peut pas grand-chose. (Et honnêtement, ce n'est pas elle qu'il fallait engueuler cette nuit-là. Bien au contraire !) Chaque aliment que nous mangeons a poussé dans une terre. Chaque gorgée d'eau est tombée un jour sous forme de pluie ou de neige. Chaque molécule d'oxygène que nous respirons vient de la photosynthèse. Dans nos vies confortables, où les rayons sont pleins et où le robinet coule quelle que soit l'actualité, nous avons oublié que face à la nature et aux climats, nos technologies, nos gouvernements, nos organisations sont des châteaux de cartes. C'est la stabilité du climat, la prévisibilité des saisons, qui a permis à un de nos ancêtres de s'arrêter un jour et de se dire : « et si on arrêtait de chasser et de cueillir. Et si on élevait et plantait ? » Événement fondateur de notre espèce. C'est la déstabilisation du climat qui peut souffler ce château de cartes. N'oublions pas notre petitesse face à la nature. N'oublions pas d'être humbles. N'oublions pas que nous ne sommes rien sans elle. Être écolo, ce n'est pas être la personne bizarre à la table de famille. C'est l'inverse : c'est être celle qui n'a pas oublié d'où vient tout le reste. Ps : vrai débat. La SNCF s’est excusée platement et offre de rembourser les billets. Ça m’arrange bien évidemment. Mais je ne trouve pas ça juste. Ils n’y sont pour rien et ne devraient pas avoir à le payer. C’est notre responsabilité collective qui est en jeu.
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Frédéric R. liked thisFrédéric R. liked thisIt was a great pleasure to co-host the #17 Dev with AI meetup at the Dashlane office this evening alongside Greg Lhotellier! 🚀 A huge thank you to all three speakers for delivering talks that were smooth, engaging, and a genuine pleasure to attend. Congratulations to Quentin B., Marwan Liani from Dashlane, and Adrien Cens! 👏
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Licenses & Certifications
Publications
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Le livre du CTO
Ametix
See publicationLe CTO ou Chief Technology Officer, c’est un peu le chef d’orchestre de la révolution numérique. Si les développeurs sont les bâtisseurs des autoroutes de l’information, alors les CTO en sont les contremaîtres. Avec l’ensemble des plus grands CTO de notre pays, nous avons monté ce think tank, le Club City-O, dont cet ouvrage est l’un des fruits. Nous espérons qu’avec celui-ci, vous découvrirez ou re-découvrirez les sujets qui sont inhérents à la vie de tout leader technique.
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Continuous Delivery eBook
StudioB / Zend
See publicationI participated in that collective eBook: Continuous Delivery eBook - Quotes from 29 DevOps Experts
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Web Project training modules [ partially in French]
See publication3-module Course on Web Project Management done for University of Bobigny
Patents
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Integration of identity access management infrastructure with zero-knowledge services
Issued US20240012933A1
A system and method for protecting user data using a key escrow service. The key escrow service may be hosted by a service provider to integrate Identity Access Management (IAM) solutions, such as Single-Sign-On (SSO) and/or System for Cross-domain Identity Management (SCIM), with a zero-knowledge service, such as a password manager or other service handling sensitive user data. In examples, secure enclave technology may be used to allow the service provider to host and manage the key escrow…
A system and method for protecting user data using a key escrow service. The key escrow service may be hosted by a service provider to integrate Identity Access Management (IAM) solutions, such as Single-Sign-On (SSO) and/or System for Cross-domain Identity Management (SCIM), with a zero-knowledge service, such as a password manager or other service handling sensitive user data. In examples, secure enclave technology may be used to allow the service provider to host and manage the key escrow service without being able to access any cryptographic key used and/or stored within a secure enclave. Accordingly, in some aspects, the service provider may have the ability to store users' secret keys for SSO and sharing keys for SCIM in a trusted, secure storage location without breaking the zero-knowledge principles of the infrastructure.
Other inventorsSee patent -
Multiple relying parties in a single-sign-on environment
Issued US12052232B2
A system and method for providing secure Single-Sign-On (SSO) authentication in a zero-knowledge architecture. A first server component may operate as a first relying party in a first SSO flow. When the user of an application successfully authenticates to a first identity provider, a first part of a secret key may be provided to the application. Additionally, a second server component may operate as a second relying party in a second SSO flow. When the first part of the secret key is received…
A system and method for providing secure Single-Sign-On (SSO) authentication in a zero-knowledge architecture. A first server component may operate as a first relying party in a first SSO flow. When the user of an application successfully authenticates to a first identity provider, a first part of a secret key may be provided to the application. Additionally, a second server component may operate as a second relying party in a second SSO flow. When the first part of the secret key is received by the application, authentication information may be provided to a second identity provider. Based on a successful authentication, a second part of the secret key may be provided to the application. The first and second parts of the secret key may be combined by the application to generate a final secret key that may be used to decipher encrypted user data.
Other inventorsSee patent -
Crowdsourced learning engine for semantic analysis of webpages
Issued US11080597B2
A method for autofilling an electronic form is provided. Elements of the electronic form are identified. A value for each identified elements of the electronic form is determined. The electronic form is automatically filled with the determined values. During the automatically filling of the electronic form, the determined value is provided in a field corresponding to each of the elements. A user input is received on the provided value. The received user input includes a correction to a first…
A method for autofilling an electronic form is provided. Elements of the electronic form are identified. A value for each identified elements of the electronic form is determined. The electronic form is automatically filled with the determined values. During the automatically filling of the electronic form, the determined value is provided in a field corresponding to each of the elements. A user input is received on the provided value. The received user input includes a correction to a first value provided in a first field of the electronic form. An autofill application is trained using the received user input.
Other inventorsSee patent -
User-session management in a zero-knowledge environment
Issued US10904004B2
Examples of the present disclosure describe systems and methods relating to user-session management in a zero-knowledge environment. When a user authenticates with a computing service to begin a session, a credential-cipher key is used to encrypt the user's authentication credentials, thereby generating session-resume data. The computing service stores the credential-cipher key, such that it is not retained by the user's computing device. Accordingly, when the user resumes the session, a resume…
Examples of the present disclosure describe systems and methods relating to user-session management in a zero-knowledge environment. When a user authenticates with a computing service to begin a session, a credential-cipher key is used to encrypt the user's authentication credentials, thereby generating session-resume data. The computing service stores the credential-cipher key, such that it is not retained by the user's computing device. Accordingly, when the user resumes the session, a resume request is generated to retrieve the credential-cipher key from the computing service, wherein the request is validated before providing the key. Upon successful validation, the computing service provides the credential-cipher key, which is then used to decrypt the session-resume data and regain access to the user's authentication credentials. The encrypted user data may then be decrypted, thereby obviating the need for the user to re-authenticate, while still retaining the privacy and security benefits of the zero-knowledge environment.
Other inventorsSee patent -
Zero-knowledge architecture between multiple systems
Issued US10848312B2
Examples of the present disclosure describe systems and methods relating to a zero-knowledge architecture between multiple systems. In an example, multiple systems may provide an application. User data of the application may be encrypted using a cryptographic key to restrict access to the user data. In some examples, the cryptographic key may not be provided to the multiple systems, thereby providing a zero-knowledge architecture. In order to ensure a user may access the cryptographic key, the…
Examples of the present disclosure describe systems and methods relating to a zero-knowledge architecture between multiple systems. In an example, multiple systems may provide an application. User data of the application may be encrypted using a cryptographic key to restrict access to the user data. In some examples, the cryptographic key may not be provided to the multiple systems, thereby providing a zero-knowledge architecture. In order to ensure a user may access the cryptographic key, the cryptographic key may be encrypted using a second cryptographic key. The encrypted representation of the cryptographic key may be provided to a first system, while the second cryptographic key may be provided to a second system. As a result, a user computing device may retrieve both the encrypted representation of the cryptographic key and the second cryptographic key from the first and second systems, respectively, in order to encrypt/decrypt user data.
Other inventorsSee patent -
Methods and systems for user authentication
Issued US10574648B2
Methods and systems for passwordless authenticating of a user are provided. A first request to access a first content is received from a first user of a first device. A first challenge is sent to the first device in response to the first request. The first challenge is resolved using a first private key associated with the first user for the first content. A first response, including a resolved challenge, is received from the first device. It is determined whether the first response is an…
Methods and systems for passwordless authenticating of a user are provided. A first request to access a first content is received from a first user of a first device. A first challenge is sent to the first device in response to the first request. The first challenge is resolved using a first private key associated with the first user for the first content. A first response, including a resolved challenge, is received from the first device. It is determined whether the first response is an acceptable response to the first challenge. Access to the first content is granted to the first user in response determining that the first response is an acceptable response.
Other inventorsSee patent -
Master password reset in a zero-knowledge architecture
Issued US10432397B2
Examples of the present disclosure describe systems and methods relating to master password reset in a zero-knowledge architecture. A master password reset may be used to regain access to encrypted user data despite not having access to the master password associated with decrypting the user data. As an example, the user data may be encrypted using a local ciphering key, wherein the key may be encrypted using a master password and stored. A second copy of the key may be stored, wherein the…
Examples of the present disclosure describe systems and methods relating to master password reset in a zero-knowledge architecture. A master password reset may be used to regain access to encrypted user data despite not having access to the master password associated with decrypting the user data. As an example, the user data may be encrypted using a local ciphering key, wherein the key may be encrypted using a master password and stored. A second copy of the key may be stored, wherein the second copy may be encrypted using a recovery key. The recovery key may then be stored by a third party. In a reset scenario in which the master password is forgotten, the recovery key may be retrieved from the third party and used to decrypt the second copy of the local ciphering key, thereby providing access to the encrypted user data without use of the master password.
Other inventorsSee patent
Organizations
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Tech Rocks
Co-organizer of the Tech Summit. Active member
- Present -
City-O
Co-Founder and President
-Co-founder of City-O the first French network of CTO https://www.journaldunet.com/solutions/emploi-rh/city-o.shtml
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Download Pentera Labs Report - revealing three new critical injection points in the ingress-nginx controller, building on Wiz’s IngressNightmare CVE. These overlooked vulnerabilities could let attackers hijack traffic, spoof headers, or reach unauthorized backend services - They exist in one of the most widely used ingress controllers in Kubernetes, putting countless environments at risk. This research highlights how small misconfigurations can lead to major exposure in modern cloud-native architectures. What’s Inside: ✅ 3 new injection vulnerabilities in ingress-nginx ✅ How attackers find and exploit CVEs in open source ✅ Actionable tips to secure your Kubernetes environment https://lnkd.in/eHtX6EdP
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