Understanding AI Security Threats

🔥 AI Security: The New Frontier of Patient Safety Cybersecurity used to mean protecting devices, networks, and data. In the age of AI, that is no longer enough. The new threat surface is the model itself. AI security now includes: • Model poisoning • Adversarial prompts • Data injection attacks • Synthetic identity creation • Algorithmic manipulation • Compromised training datasets • Unauthorized model extraction • Real-time clinical guidance distortion If your AI is compromised, your patient care is compromised. It’s that simple. Forward-looking healthcare leaders are pivoting from: “Protect the system” → to → “Protect the intelligence behind the system.” What we protect must now include: ✔️ Model integrity ✔️ Training data lineage ✔️ API security ✔️ Prompt security ✔️ Real-time monitoring of drift ✔️ Audit trails for algorithmic decisions ✔️ Red-team testing for AI vulnerabilities In 2026, AI security will become the new patient safety. Leaders who don’t understand AI risk cannot ensure clinical safety. — Khalid Turk MBA, PMP, CHCIO, FCHIME Building systems that work, teams that thrive, and cultures that endure.

13 national cyber agencies from around the world, led by #ACSC, have collaborated on a guide for secure use of a range of "AI" technologies, and it is definitely worth a read! "Engaging with Artificial Intelligence" was written with collaboration from Australian Cyber Security Centre, along with the Cybersecurity and Infrastructure Security Agency (#CISA), FBI, NSA, NCSC-UK, CCCS, NCSC-NZ, CERT NZ, BSI, INCD, NISC, NCSC-NO, CSA, and SNCC, so you would expect this to be a tome, but it's only 15 pages! It is refreshing to see that the article is not solely focused on LLMs (eg. ChatGPT), but defines Artificial Intelligence to include Machine Learning, Natural Language Processing, and Generative AI (LLMs), while acknowledging there are other sub-fields as well. The challenges identified (with actual real-world examples!) are: 🚩 Data Poisoning of an AI Model: manipulating an AI model's training data, leading to incorrect, biased, or malicious outputs 🚩 Input Manipulation Attacks: includes prompt injection and adversarial examples, where malicious inputs are used to hijack AI model outputs or cause misclassifications 🚩 Generative AI Hallucinations: generating inaccurate or factually incorrect information 🚩 Privacy and Intellectual Property Concerns: challenges in ensuring the security of sensitive data, including personal and intellectual property, within AI systems 🚩 Model Stealing Attack: creating replicas of AI models using the outputs of existing systems, raising intellectual property and privacy issues The suggested mitigations include generic (but useful!) cybersecurity advice as well as AI-specific advice: 🔐 Implement cyber security frameworks 🔐 Assess privacy and data protection impact 🔐 Enforce phishing-resistant multi-factor authentication 🔐 Manage privileged access on a need-to-know basis 🔐 Maintain backups of AI models and training data 🔐 Conduct trials for AI systems 🔐 Use secure-by-design principles and evaluate supply chains 🔐 Understand AI system limitations 🔐 Ensure qualified staff manage AI systems 🔐 Perform regular health checks and manage data drift 🔐 Implement logging and monitoring for AI systems 🔐 Develop an incident response plan for AI systems This guide is a great practical resource for users of AI systems. I would interested to know if there are any incident response plans specifically written for AI systems - are there any available from a reputable source?

AI-powered malware isn’t science fiction—it’s here, and it’s changing cybersecurity. This new breed of malware can learn and adapt to bypass traditional security measures, making it harder than ever to detect and neutralize. Here’s the reality: AI-powered malware can: 👉 Outsmart conventional antivirus software 👉 Evade detection by constantly evolving 👉 Exploit vulnerabilities before your team even knows they exist But there’s hope. 🛡️ Here’s what you need to know to combat this evolving threat: 1️⃣ Shift from Reactive to Proactive Defense → Relying solely on traditional tools? It’s time to upgrade. AI-powered malware demands AI-powered security solutions that can learn and adapt just as fast. 2️⃣ Focus on Behavioral Analysis → This malware changes its signature constantly. Instead of relying on patterns, use tools that detect abnormal behaviors to spot threats in real time. 3️⃣ Embrace Zero Trust Architecture → Assume no one is trustworthy by default. Implement strict access controls and continuous verification to minimize the chances of an attack succeeding. 4️⃣ Invest in Threat Intelligence → Keep up with the latest in cyber threats. Real-time threat intelligence will keep you ahead of evolving tactics, making it easier to respond to new threats. 5️⃣ Prepare for the Unexpected → Even with the best defenses, breaches can happen. Have a strong incident response plan in place to minimize damage and recover quickly. AI-powered malware is evolving. But with the right strategies and tools, so can your defenses. 👉 Ready to stay ahead of AI-driven threats? Let’s talk about how to future-proof your cybersecurity approach.

AI is rapidly becoming the nerve-center of how we build, sell, and serve—but that also makes it a bullseye. Before you can defend your models, you need to understand how attackers break them. Here are the five most common vectors I’m seeing in the wild: 1️⃣ Prompt Injection & Jailbreaks – Hidden instructions in seemingly harmless text or images can trick a chatbot into leaking data or taking unintended actions. 2️⃣ Data / Model Poisoning – Adversaries slip malicious samples into your training or fine-tuning set, planting logic bombs that detonate after deployment. 3️⃣ Supply-Chain Manipulation – LLMs sometimes “hallucinate” package names; attackers register those libraries so an unwary dev installs malware straight from npm or PyPI. 4️⃣ Model Theft & Extraction – Bulk-scraping outputs or abusing unsecured endpoints can replicate proprietary capabilities and drain your competitive moat. 5️⃣ Membership-Inference & Privacy Leakage – Researchers keep showing they can guess whether a sensitive record was in the training set, turning personal data into low-hanging fruit. Knowing the playbook is half the battle. Stay tuned—and start threat-modeling your AI today. 🔒🤖

"Technologists and policymakers are increasingly seized with the importance of addressing AI Loss Of Control (LOC) risk—a hypothetical state in which an AI system diverges from authorized constraints to the extent that the human operator is no longer able to prevent, constrain, or revert undesired and unintended outcomes. However, significant gaps remain in how policymakers, the AI industry and AI security and safety researchers understand, anticipate, and perceive this risk. As these systems continue to gain power and capability, even a five percent probability that the worst-case AI LOC scenario materializes should be enough to compel decision-makers to treat this risk category as a national, human, and economic security priority. To address this gap, this paper proposes applying the Indications & Warning (I&W) methodology—used by the intelligence community to detect, track, and warn of impending significant threats—for monitoring AI LOC risk. The framework distinguishes between potential AI LOC indicators (theoretical behaviors signaling potential LOC) and actual indications (documented evidence that these patterns are occurring in reality)[...] To monitor AI LOC risk in particular, this paper proposes seven potential indicators:   • Scheming [...] • Manipulation [...] • Deception [...] • Self-Preserving Behavior [...] • Unauthorized Resource Acquisition [...] • Goal Misgeneralization [...] • Model and Behavior Drift [...] [...] A growing body of evidence, laid out in this paper, finds that AI systems can: • Conceal their actions and fabricate data to deceive the human operator • Identify vulnerable users and target them with manipulative strategies • Learn deception through reinforcement learning rewards • Strategically adjust behavior when they detect being evaluated • Rewrite their own system prompt to preserve their goals, copy their weights to external servers, and delete successor models • Conceal their reasoning from interpretability tools • Gradually lose their alignment properties over deployment cycles • Pursue unintended goals that succeed in training but fail in novel contexts • Optimize for code completion while systematically failing in security objectives • Circumvent shutdown mechanisms to continue task execution • Strategically alter behavior to evade evaluation and preserve deployment viability" Lots more in the document attached. Great work from Mariami T., Ritika Verma, and Steven M. Kelly at the Institute for Security and Technology (IST). I'm glad that I could play a role alongside some other members of the working group.

AI agents aren’t just a productivity upgrade. They’re a new attack surface. We’ve spent years worrying about chatbots leaking information. That problem is real, but it’s not the hard part. The real risk shows up when agents are given system access and start operating like virtual employees. Because agents don’t just read data. They can edit records, initiate transactions, modify workflows, and trigger downstream systems — often at machine speed. Attackers are already adapting. One of the most underestimated risks right now is prompt injection: hiding malicious instructions inside content an agent is allowed to see. When an agent has credentials and tool access, a single poisoned input can turn into unauthorized actions across multiple systems. That’s the shift most teams haven’t internalized yet. AI security isn’t about protecting a model. It’s about protecting identity, access, data, and execution paths — end to end. In an agentic environment, you have to assume agents will be tricked, inputs will be hostile, permissions will be abused, and failures won’t look like traditional breaches. Which means security design has to change. — Agents should never have standing privileges — Credentials must be isolated from humans and services — Every agent action needs to be logged, attributable, and replayable — Anomaly detection has to be tuned to agent behavior, not human behavior — Zero trust has to apply at the data, prompt, tool, API, and workflow layers And here’s the uncomfortable reality: The threat landscape for AI agents is still forming. We don’t fully understand it yet. That’s not a reason to slow down. It’s a reason to design defensively. Assume compromise. Expect emergent behavior. Instrument everything. If an agent can take action on your behalf, ask yourself: what systems can it touch, what data can it see, what happens if its instructions are poisoned, how quickly would you detect abnormal behavior, and could you prove — after the fact — exactly what it did and why? If those answers aren’t crisp, you don’t have an AI strategy. You have liability. The cybersecurity attorneys at Buchanan Ingersoll & Rooney PC can help. Have questions about securing AI tools? Reach out to us: cyber@bipc.com #AI #Cybersecurity #AISecurity #AgenticAI #ZeroTrust #AIGovernance Dr. Chase Cunningham Chris Hughes NetDiligence® Shannon Noonan The Cyber Guild Quorum Cyber GuidePoint Security Expel Airlock Digital Timothy Horigan AmTrust Financial Services, Inc. ANV Coalition, Inc. Beazley Berkley Technology Underwriters (a Berkley Company) Erin Eisenrich Brian Zimmer Michael South David Beabout Cory Simpson Maj Gen Matteo Martemucci, USAF Heather McMahon TJ White Nick Andersen Sean Plankey George A. Guillermo Christensen Hala Nelson Dan Van Wagenen Kurt Sanger David Eapen Andria Adigwe, CIPP/US Tiffany Yeung Jillian Cash Jacqueline Jonczyk, CIPP/US Kellen Carleton Harry Valetk Crum & Forster VeridatAI, Inc.

⚠️ Stop these 9 AI threats before it’s too late. Most teams are racing to adopt AI without realizing they’re opening the door to a whole new category of risks. I’ve seen companies get burned by AI hallucinations in customer service. I’ve watched executives fall for deepfake scams. I’ve seen proprietary code accidentally leaked through ChatGPT prompts. Here’s what keeps me up at night: while we’re all excited about AI’s potential, very few organizations have updated their security playbooks to match this new reality. We’re using yesterday’s defenses against tomorrow’s threats. 📌 The 9 AI Security Risks Every Leader Should Know: 1. HALLUCINATIONS Your AI confidently gives wrong answers. Models predict likely words, not facts. They don't say "I don't know." → Fix: Add verification steps. Require citations. Train users not to trust blindly. 2. PILL EXPOSURE Private data (names, emails, IDs) leaks unintentionally from your prompts or responses. → Fix: Mask sensitive data. Audit logs. Use separate environments for testing. 3. DEEPFAKES & SYNTHETIC MEDIA Fake videos/audio impersonating executives. Scams. Misinformation. → Fix: Detection tools. Watermarking. Train employees on verification. 4. PROMPT INJECTION & DATA LEAKS Attackers exploit AI inputs to access data or change commands. → Fix: Sanitize inputs. Limit model access. Monitor unusual queries. 5. SHADOW AI Employees using unauthorized AI tools without IT knowing. → Fix: AI governance policy. Approved tools list. Regular audits. 6. MODEL BIAS AI supports discrimination or unfair decisions trained on biased data. → Fix: Audit training data. Test for bias. Diverse evaluation teams. 7. IP LEAKAGE Internal code or proprietary data leaks via AI systems. → Fix: Don't paste internal data into public AI. Use private deployments. 8. COMPLIANCE & REGULATION Data privacy violations or AI-related legal breaches. → Fix: Know your regulations (GDPR, DPDPA, AI Act). Document decisions. 9. THIRD-PARTY VULNERABILITIES Exposure via vendors, APIs, or model integrations you depend on. → Fix: Vet vendors. Monitor integrations. Have backup providers 📥 Get Free Access to My AI Data Security Guide Here: https://lnkd.in/gtenUagT Save this post. Share it with your team. Because the best defense against AI risks is knowing they exist in the first place. ___________________________________________ 👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

In the landscape of AI, robust governance, risk, and security frameworks are essential to manage various risks. However, a silent yet potent threat looms: Prompt Injection. Prompt Injection exploits the design of large language models (LLMs), which treat instructions and data within the same context window. Natural language sanitization is nearly impossible, highlighting the need for architectural defenses. If these defenses are not implemented correctly, they pose significant threats to an organization's reputation, compliance, and bottom line. For instance, a chatbot designed to handle client queries 24/7 could be manipulated into revealing company secrets, generating offensive content, or connecting with internal systems. To address these challenges, a Defense-in-Depth approach is crucial for implementing AI use cases: 1. Zero-Trust for AI: Assume every prompt is hostile and establish mechanisms to validate all inputs. 2. Prompt Firewalls: Implement pattern recognition for both incoming prompts and outgoing responses. 3. Architectural Separation: Ensure no LLM has direct access to databases and APIs. It should communicate with your data without direct interaction, with an intermediate layer that includes all necessary security controls. 4. AI Bodyguards: Leverage specialized security AI models to screen prompts and responses for malicious intent. 5. Continuous Stress Testing: Engage "red teams" to actively attempt to breach your AI's defenses, identifying weaknesses before real attackers do. The future of AI is promising, but only if it is secure. Consider how you are fortifying your AI adoption. #riskmanagement #AIGovernance #cybersecurity

𝐀𝐫𝐞 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 , 𝐀𝐰𝐚𝐫𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐫𝐢𝐬𝐤𝐬 𝐭𝐡𝐞𝐢𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐚𝐫𝐫𝐲? AI increases the pace of business. With that it also increases the attack surface. If AI affects your data, decisions or workflows, The risks associated with i are now business risks. Leaders do not have to build models. They need to understand where models fail. I am sharing the 𝟏𝟎 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬, Every leader should understand. 𝟏-𝐃𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲  AI sees customer data, internal docs and logs. Know what data is used and who can access it. 𝟐-𝐌𝐨𝐝𝐞𝐥 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐩𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠  Bad data can quietly change model behaviour. Ask how training data is protected. 𝟑-𝐏𝐫𝐨𝐦𝐩𝐭 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧  Inputs can trick models into breaking rules. Controls must exist outside the model. 𝟒-𝐎𝐮𝐭𝐩𝐮𝐭 𝐝𝐚𝐭𝐚 𝐥𝐞𝐚𝐤𝐚𝐠𝐞  Models can repeat sensitive information. Set strict rules on what enters AI tools. 𝟓-𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐚𝐧𝐝 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬  AI agents run with powerful keys. Least privilege is critical. 𝟔-𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐚𝐧𝐝 𝐓𝐡𝐢𝐫𝐝 ‑ 𝐏𝐚𝐫𝐭𝐲 𝐌𝐨𝐝𝐞𝐥𝐬  Third party models can hide vulnerabilities. Security reviews still apply. 𝟕-𝐑𝐨𝐛𝐮𝐬𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐟𝐨𝐫 𝐀𝐈  Dashboards miss behaviour changes. Expect visibility into inputs and outputs. 𝟖-𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐀𝐭𝐭𝐚𝐜𝐤𝐬 𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬  Small changes can cause wrong results. High risk use cases need extra testing. 𝟗-𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐢𝐬𝐤 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬  Policies define ownership and escalation. Frameworks reduce chaos. 𝟏𝟎-𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬  Know how to pause, roll back and communicate. Treat AI incidents like cyber incidents. AI is not just a productivity tool. Now it is part of your security perimeter. Which of these areas would you prioritize for deeper understanding? --------- Hi, I'm Harris D. Schwartz, Fractional CISO and Cybersecurity Leader. I help CEOs and executive teams strengthen their security posture and build resilient, compliant organizations. With 𝟑𝟎+ 𝐲𝐞𝐚𝐫𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐍𝐈𝐒𝐓, 𝐈𝐒𝐎, 𝐏𝐂𝐈, 𝐚𝐧𝐝 𝐆𝐃𝐏𝐑, I know how the right security decisions reduce risk and protect growth. If you are planning how your security program needs to evolve in 2026, this is the right time to have that conversation. #CyberSecurity #AISecurity #AIrisk #CISO #SecurityLeadership #CyberRisk

AI security is evolving rapidly, and OWASP’s Agentic AI Threat Model is a crucial step toward securing autonomous systems. As AI agents take on more complex roles - executing tasks, interacting with external tools, and even making decisions, the risks extend beyond traditional security concerns like data leakage or model vulnerabilities. The key threats identified here, such as memory poisoning, tool misuse, and cascading hallucinations, highlight how AI autonomy introduces new attack vectors that security teams must address. The Real-World Challenge - From Theory to Implementation!! While this framework is invaluable, the challenge is operationalizing these mitigations within organizations. Security teams already struggle to keep up with conventional AI risks, and agentic AI adds an entirely new layer of complexity. Some practical considerations: 1. Monitoring & Detection Lag Behind Traditional cybersecurity tools are not built to handle the nuances of agentic AI threats. AI behavior can be unpredictable, making anomaly detection harder. Organizations will need specialized AI security monitoring that tracks how agents use memory, tools, and decision-making processes. 2. Balancing Security & Functionality AI systems that are too locked down lose their utility. For example, limiting tool execution can prevent misuse but may also hinder productivity. Companies will need dynamic security policies that adapt based on context, risk, and the agent’s role. 3. Developer Education & Secure AI Practices AI developers are rarely trained in security, and security professionals are often unfamiliar with how AI agents function. Bridging this gap is critical. Organizations should integrate security principles directly into AI development workflows, similar to how DevSecOps transformed traditional software security. 4. Regulation & Compliance Pressure As governments catch up, regulations will demand stricter controls over AI behavior. Implementing cryptographic logging, authentication measures, and human-in-the-loop oversight today will not just reduce risk but also future-proof AI deployments against upcoming legal requirements. What’s Next? Security leaders should start by mapping OWASP® Foundation's threats to their AI systems, identifying the highest-risk areas, and prioritizing mitigations that align with business needs. Investing in AI security tooling and expertise now will prevent costly incidents down the road. How are you thinking about securing agentic AI in your organization? Are current security frameworks keeping up?