Emerging Risks in Security Algorithms

🤖 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞’𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 – 𝐛𝐮𝐭 𝐡𝐚𝐫𝐝𝐥𝐲 𝐚𝐧𝐲𝐨𝐧𝐞 𝐢𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲. 🔐 As a CISO, I see the rapid rollout of AI tools across organizations. But what often gets overlooked are the unique security risks these systems introduce. Unlike traditional software, AI systems create entirely new attack surfaces like: ⚠️ 𝐃𝐚𝐭𝐚 𝐩𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠: Just a few manipulated data points can alter model behavior in subtle but dangerous ways. ⚠️ 𝐏𝐫𝐨𝐦𝐩𝐭 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧: Malicious inputs can trick models into revealing sensitive data or bypassing safeguards. ⚠️ 𝐒𝐡𝐚𝐝𝐨𝐰 𝐀𝐈: Unofficial tools used without oversight can undermine compliance and governance entirely. We urgently need new ways of thinking and structured frameworks to embed security from the very beginning. 📘 A great starting point is the new 𝐒𝐀𝐈𝐋 (𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐈 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞) Framework whitepaper by Pillar Security. It provides actionable guidance for integrating security across every phase of the AI lifecycle from planning and development to deployment and monitoring. 🔍 𝐖𝐡𝐚𝐭 𝐈 𝐩𝐚𝐫𝐭𝐢𝐜𝐮𝐥𝐚𝐫𝐥𝐲 𝐯𝐚𝐥𝐮𝐞: ✅ More than 𝟕𝟎 𝐀𝐈-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐫𝐢𝐬𝐤𝐬, mapped and categorized ✅ A clear phase-based structure: Plan – Build – Test – Deploy – Operate – Monitor ✅ Alignment with current standards like ISO 42001, NIST AI RMF and the OWASP Top 10 for LLMs 👉 Read the full whitepaper here: https://lnkd.in/ebtbztQC How are you approaching AI risk in your organization? Have you already started implementing a structured AI security framework? #AIsecurity #CISO #SAILframework #SecureAI #Governance #MLops #Cybersecurity #AIrisks

🚨 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐓𝐡𝐫𝐞𝐚𝐭𝐬 𝐭𝐨 𝐀𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 𝐭𝐨 𝐊𝐧𝐨𝐰 🚨 Imagine your AI system making decisions based on data that's been subtly tampered with. Sounds like science fiction? Think again. Security researcher 𝐽𝑜ℎ𝑎𝑛𝑛 𝑅𝑒ℎ𝑏𝑒𝑟𝑔𝑒𝑟 recently uncovered vulnerabilities in AI models like ChatGPT that could allow malicious actors to inject harmful instructions and extract sensitive data over time. As AI becomes integral to our decision-making processes, we have to ask: 𝐇𝐨𝐰 𝐬𝐞𝐜𝐮𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞𝐬𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐬𝐭𝐞𝐩𝐬 𝐜𝐚𝐧 𝐰𝐞 𝐭𝐚𝐤𝐞 𝐭𝐨 𝐩𝐫𝐨𝐭𝐞𝐜𝐭 𝐭𝐡𝐞𝐦? 🔍 𝐓𝐡𝐞 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞: 🛑 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐢𝐬𝐤𝐬: AI models are susceptible to adversarial inputs- malicious data crafted to deceive or influence system outputs. 🕵️♂️ 𝐒𝐢𝐥𝐞𝐧𝐭 𝐄𝐱𝐩𝐥𝐨𝐢𝐭𝐚𝐭𝐢𝐨𝐧: Attackers might manipulate AI behavior or siphon off confidential information without immediate detection. 🔒 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: Firewalls and standard cybersecurity measures aren't enough. We need strategies that ensure AI systems process and learn from trustworthy data. 🤔 𝐏𝐨𝐢𝐧𝐭𝐬 𝐭𝐨 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫: 🔓 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲 𝐯𝐬. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: How do we balance the openness that fosters AI innovation with the need to protect against exploitation? 🤝 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: What roles do developers, organizations, and users play in safeguarding AI systems? 🚀 𝐅𝐮𝐭𝐮𝐫𝐞 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: If AI can be manipulated today, what does this mean for more advanced systems tomorrow? 🔑 𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐖𝐞 𝐃𝐨? 📖 𝐒𝐭𝐚𝐲 𝐈𝐧𝐟𝐨𝐫𝐦𝐞𝐝: Keep abreast of the latest developments in AI security to understand potential vulnerabilities. 🛠️ 𝐏𝐫𝐨𝐦𝐨𝐭𝐞 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬: Encourage the adoption of secure coding practices and regular audits in AI development. 🤝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 𝐨𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: Work with industry peers, cybersecurity experts, and policymakers to develop robust defense mechanisms. In a world where AI influences everything from business strategies to personal recommendations, ensuring the integrity of these systems is paramount. 𝐂𝐚𝐧 𝐰𝐞 𝐚𝐟𝐟𝐨𝐫𝐝 𝐭𝐨 𝐨𝐯𝐞𝐫𝐥𝐨𝐨𝐤 𝐭𝐡𝐞 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐭𝐨𝐨𝐥𝐬 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐨𝐮𝐫 𝐟𝐮𝐭𝐮𝐫𝐞? 💬 𝐋𝐞𝐭'𝐬 𝐬𝐭𝐚𝐫𝐭 𝐚 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧! What measures do you believe are essential in securing AI against emerging threats? Share your thoughts below! 🔽 🔗 Link to Johann Rehberger's analysis: https://lnkd.in/d9QVwE_5 #AI #Cybersecurity #DataIntegrity #FutureTech #Collaboration #AIEthics ¦ Deloitte

❗ Microsoft’s new research highlights an emerging AI risk: Recommendation poisoning. Attackers are exploiting features like “Summarize with AI” buttons to insert hidden instructions into an AI assistant’s memory. Over time, those instructions can influence what the AI recommends, prioritizes, or frames as credible. No breach or ransomware needed! Subtle but insidious. Not unlike black hat SEO. More than 50 prompt-based poisoning attempts across 31 companies and 14 industries have already been observed. AI systems are increasingly embedded in decision workflows ranging from vendor selection to financial analysis to healthcare guidance. If recommendations can be persistently nudged without user awareness, the integrity of those decisions is at stake. A few implications for leaders: • AI memory is now an attack surface. Persistence creates both personalization and vulnerability. • Security protocols and AI training need to cover AI recommendation poisoning. It's possible to address it if it's already happened as well as to take steps to prevent it. • Governance must expand beyond data to behavior. It’s about both what models are trained and how they’re steered over time. AI doesn’t just answer questions. It shapes choices. Safeguarding its integrity not only a technical issue but also a business imperative. Learn more about the research here: https://lnkd.in/eNA8dux7 Learn more about memory-rich AI here: https://lnkd.in/eTVKX3Jz #ai #risk #governance #workflow #strategy #digitaltransformation #contentstrategy

AI is no longer just a productivity booster — it’s a security risk multiplier. Yet most organizations are still assessing AI like traditional IT — and that’s a costly mistake. An AI Security Risk Assessment must go beyond infrastructure and focus on: ✅ Model Hallucination & Manipulation (prompt injection, jailbreaks) ✅ Sensitive Data Leakage (accidental training, unlogged API calls) ✅ Shadow AI & Unapproved Integrations ✅ Compliance Risks — GDPR, DPDP, ISO 42001, NIST AI RMF ✅ AI Supply Chain & Third-Party Model Trustworthiness ✅ Continuous Monitoring — not one-time assessment Companies that treat AI risk as a checkbox exercise today… will face a crisis tomorrow. AI is a strategic advantage — only if governed like a critical asset, not a cool tool. Are you already integrating AI risk into your enterprise GRC strategy? --- #AI #AIsecurity #AIGovernance #AIRiskAssessment #CyberSecurity #ISO42001 #NIST #GenAI #DataProtection #AICompliance #GRC #CISO #RiskManagement

AI isn’t your biggest advantage in 2026. It might be your biggest vulnerability. Everyone is racing to adopt AI. Very few are securing it. Here are 10 AI security failures that could quietly destroy enterprises in 2026: 1. Prompt Injection (Critical) One malicious prompt… and your system does exactly what it shouldn’t. 2. Data Poisoning (Critical) Bad data in → dangerous decisions out. 3. Model Theft (High) Your AI isn’t hacked… it’s copied. 4. Agent Hijacking (High) Autonomous agents executing tasks you never approved. 5. Sensitive Data Leakage (High) Employees feeding confidential data into AI tools without realizing the risk. 6. Model Inversion (High) Attackers pulling hidden training data straight from your model. 7. Supply Chain Attacks (High) One compromised tool = full system exposure. 8. Shadow AI (High) Teams using unapproved AI tools outside your visibility. 9. Adversarial Inputs (Medium–High) Small changes → completely wrong outputs. 10. Hallucination Exploitation (Medium–High) Confidently wrong answers driving real-world decisions. AI doesn’t fail loudly. It fails silently… until it’s too late. The real risk isn’t adopting AI. It’s adopting it without control. The companies that win in this era won’t be the fastest. They’ll be the ones that are secure, governed, and trusted. If your AI made a critical wrong decision today… would you even catch it in time? 👉 Follow Marcel Velica for more insights on AI, cybersecurity, and growth 🔁 Share this with others who need to see it If you want short daily thoughts, quick threat observations, and real-time discussions, follow me on X as well →https://x.com/MarcelVelica

Something feels safe about AI systems. Clean interface. Confident answers. Fast decisions. But behind the screen, a different story is unfolding. Many organizations are racing to deploy Generative AI. Few are slowing down to examine the risks hidden inside the architecture. Security teams are beginning to realize a difficult truth. The biggest risk is not the model. The biggest risk is how systems trust the model. The 𝐎𝐖𝐀𝐒𝐏 𝐓𝐨𝐩 10 𝐟𝐨𝐫 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 highlights where things quietly break. These risks are already appearing in production environments. → 𝐎𝐯𝐞𝐫𝐫𝐞𝐥𝐢𝐚𝐧𝐜𝐞 𝐨𝐧 𝐋𝐋𝐌 𝐎𝐮𝐭𝐩𝐮𝐭 • Blind trust in generated responses • Hallucinated code entering production • Fabricated citations treated as facts → 𝐏𝐫𝐨𝐦𝐩𝐭 𝐈𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧 • Malicious instructions hidden in prompts • System behavior manipulation • Instruction override attacks → 𝐌𝐨𝐝𝐞𝐥 𝐓𝐡𝐞𝐟𝐭 • Reverse engineering through repeated queries • Model inversion techniques • Membership inference attacks → 𝐄𝐱𝐜𝐞𝐬𝐬𝐢𝐯𝐞 𝐀𝐠𝐞𝐧𝐜𝐲 • AI agents granted broad permissions • Autonomous file deletion • Automated email execution → 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠 • Malicious data inserted into training pipelines • Biased or manipulated model behavior • Hidden backdoors in datasets → 𝐈𝐧𝐬𝐞𝐜𝐮𝐫𝐞 𝐎𝐮𝐭𝐩𝐮𝐭 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 • Unvalidated model responses used downstream • Injection into applications • XSS, SSRF, privilege escalation, RCE → 𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐃𝐢𝐬𝐜𝐥𝐨𝐬𝐮𝐫𝐞 • Leakage of credentials or PII • Exposure of system prompts • Reconstruction of memorized data → 𝐌𝐨𝐝𝐞𝐥 𝐃𝐞𝐧𝐢𝐚𝐥 𝐨𝐟 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 • Expensive recursive prompts • Latency spikes • Rapid cost escalation → 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐕𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 • Compromised model weights • Risky third party datasets • Unsafe external APIs →  𝐈𝐧𝐬𝐞𝐜𝐮𝐫𝐞 𝐏𝐥𝐮𝐠𝐢𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 • Weak input validation • Excessive OAuth permissions • One plugin compromising entire systems Generative AI is powerful. But power without governance creates silent vulnerabilities. Security in AI is no longer optional. It is architecture. Cyber Leadership Academy Follow Vijay Banda for more insights

Large Language Model (LLM) Poisoning: The Next Cybersecurity Battleground Artificial Intelligence is transforming how organizations operate, automate decisions, and analyze data. But as adoption accelerates, so do the attack surfaces surrounding AI systems. One emerging threat that deserves more attention is LLM poisoning. LLM poisoning occurs when malicious or manipulated data is intentionally introduced into the training or fine-tuning process of a large language model. Because these systems learn patterns directly from data, corrupted inputs can quietly influence outputs — often without immediate detection. Unlike traditional cyberattacks that exploit software vulnerabilities, LLM poisoning targets trust itself. A poisoned model may: 🔹 Generate biased or misleading responses 🔹 Leak sensitive information 🔹 Produce insecure code recommendations 🔹 Embed subtle manipulation aligned with an attacker’s objectives The risk becomes even greater as organizations adopt retrieval-augmented generation (RAG), external datasets, and continuous learning pipelines. If data sources are not validated, attackers may influence models indirectly through compromised documentation, repositories, or public content. Mitigating LLM poisoning requires a shift in mindset. Security teams must begin treating training data as critical infrastructure. Effective defenses include: ✅ Data provenance validation ✅ Secure dataset curation and access controls ✅ Continuous output monitoring and anomaly detection ✅ Model evaluation and red-team testing ✅ Strong AI governance and guardrails AI security is no longer just about protecting systems — it’s about protecting learning processes. As LLMs increasingly support business decisions, development workflows, and cybersecurity operations themselves, ensuring model integrity will become a defining challenge of modern security programs. The question is no longer if AI systems will be targeted — but how prepared we are when they are. #Cybersecurity #AI #ArtificialIntelligence #LLMSecurity #MachineLearningSecurity #AISecurity #RiskManagement #DataSecurity #InfoSec #EmergingThreats

The Wrapper Conundrum: Why #PQC "Bolt-Ons" Are a Governance Time Bomb The Harvest Now, Decrypt Later (#HNDL) risk is no longer theoretical. For industries with data that must remain secret for decades, the breach has already begun. In response, we are seeing a surge in PQC wrappers—gateway or overlay encryption layers that promise quantum-safe “corridors” without touching a line of legacy code. For a busy CISO, it sounds like the ultimate win. ITS NOT But from a governance and risk perspective, a wrapper is a stepping stone, not a destination. It is a necessary mitigation for today, but it will not survive the coming decade of regulatory and architectural change. The wrapper solves the transport problem, not the cryptography problem. ⚠️ The liability lies in what the wrapper leaves behind: 📅 The Compliance Cliff Agencies like the National Security Agency have already set the clock through the Commercial National Security Algorithm Suite 2.0 roadmap. By roughly 2030–2033, classical public-key algorithms such as RSA and Elliptic Curve Cryptography will no longer be approved for national security systems. A wrapper that shields legacy code while leaving classical cryptography running in situ doesn’t solve the problem. It simply hides a non-compliant ghost in the machine that will fail an audit in five years. 🔄 The Inevitable Shift Toward Native PQC Hybrid cryptography is currently the pragmatic deployment model. But over time, as regulators tighten requirements, hybrid deployments are expected to give way to native Post-Quantum Cryptography implementations—particularly as classical fallbacks are phased out. When that happens, bolt-on wrappers will be viewed not as mitigation, but as architectural debt. 🔓 The Three-Domain Exposure A wrapper secures Data in Motion, and in some cases storage gateways. But the application logic and cryptographic libraries underneath often remain classical. If your system still processes or stores sensitive data using legacy cryptography, the “quantum-safe highway” still leads to a vulnerable destination. 🌍 Sovereign Fragmentation The global quantum landscape is diverging. The West is scaling the mathematics of PQC. China is investing heavily in the physical infrastructure of Quantum Key Distribution. A static wrapper cannot navigate a future where systems must adapt to different sovereign cryptographic regimes. You cannot bolt on the level of cryptographic agility this environment will demand. ⚖️ The Verdict A wrapper is an excellent tool to slow the immediate bleed of HNDL. Use it to buy time - if you must. But don’t mistake a temporary bridge for a permanent foundation. True quantum resilience requires cryptographic agility—not just a better box around legacy code. If you’re not planning to address the “classical in situ” problem, you’re not managing quantum risk. You’re simply delaying the inevitable. #QuantumComputing #RiskManagement #Cryptography #HNDL #CNSA20 SITG-Consulting

Most AI security discussions still center on single-agent threats: prompt injection, jailbreaks, tool misuse. But as systems shift to multi-agent architectures, a different class of risk emerges. One that lives not in individual models, but in how they delegate, recurse, and trust each other. I wrote about four exploit patterns that matter in this space:  → Cross-agent prompt amplification  → Recursive loop reinforcement  → Delegated privilege escalation  → Shared memory poisoning And why the real answer isn't better prompts — it's architectural. ✍ Full post: https://lnkd.in/g4a2qBmH  #AIRedTeaming #LLMSecurity #MultiAgent #OffensiveSecurity