Key ASI Risks and Control Challenges

In an era where many use AI to 'summarize and synthesize' to keep up with what's happening, some documents are worth a careful read. This is one. 📕 The OWASP Top 10 for Agentic Applications 2026 outlines the most critical security risks introduced by autonomous AI agents and provides practical guidance for mitigating them. 👉 ASI01 – Agent Goal Hijack Attackers manipulate an agent’s goals, instructions, or decision pathways—often via hidden or adversarial inputs—redirecting its autonomous behavior. 👉 ASI02 – Tool Misuse & Exploitation Agents misuse legitimate tools due to injected instructions, misalignment, or overly broad capabilities, leading to data leakage, destructive actions, or workflow hijacking. 👉 ASI03 – Identity & Privilege Abuse Weak identity boundaries or inherited credentials allow agents to escalate privileges, misuse access, or act under improper authority. 👉 ASI04 – Agentic Supply Chain Vulnerabilities Malicious or compromised third-party tools, models, agents, or dynamic components introduce unsafe behaviors, hidden instructions, or backdoors into agent workflows. 👉 ASI05 – Unexpected Code Execution (RCE) Unsafe code generation or execution pathways enable attackers to escalate prompts into harmful code execution, compromising hosts or environments. 👉 ASI06 – Memory & Context Poisoning Adversaries corrupt an agent’s stored memory, context, or retrieval sources, causing future reasoning, planning, or tool use to become unsafe or biased. 👉 ASI07 – Insecure Inter-Agent Communication Poor authentication, integrity checks, or protocol controls allow spoofed, tampered, or replayed messages between agents, leading to misinformation or unauthorized actions. 👉 ASI08 – Cascading Failures A single poisoned input, hallucination, or compromised component propagates across interconnected agents, amplifying small faults into system-wide failures. 👉 ASI09 – Human-Agent Trust Exploitation Attackers exploit human trust, authority bias, or fabricated rationales to manipulate users into approving harmful actions or sharing sensitive information. 👉 ASI10 – Rogue Agents Agents that become compromised or misaligned deviate from intended behavior—pursuing harmful objectives, hijacking workflows, or acting autonomously beyond approved scope. The OWASP® Foundation has been doing some amazing work on AI security, and this resource is another great example. For AI assurance professionals, these documents are a valuable resource for us and our clients. #agenticai #aisecurity #agentsecurity Khoa Lam, Ayşegül Güzel, Max Rizzuto, Dinah Rabe, Patrick Sullivan, Danny Manimbo, Walter Haydock, Patrick Hall

Agentic AI Security Threats, Defenses, Evaluation, and Open Challenges. As we all know, we're in the "decade" of Agents (queue Karpathy), with excitement around a near infinite set of use cases and potential. That said, as noted by the OWASP GenAI Security Project and others, Agentic AI also poses numerous threats and security challenges. This is an excellent paper on the topic, highlighting key vulnerabilities that have already been discovered (cc: Zenity, Astrix Security Ken Huang, Idan Habler, PhD, Vineeth Sai Narajala, Gadi Evron, etc.). It also provides a comprehensive taxonomy of security threats to Agentic AI, including prompt injection (both direct and indirect), jailbreaks, tool abuse, and other related threats. It covers key threats to emerging agentic protocols such as MCP and A2A. It then outlines fundamental defenses and security controls, including prompt-injection-resistant designs, runtime behavior monitoring, policy filtering, and governance. This is a well-written paper that is well-researched, thoroughly cited, and accompanied by intuitive diagrams that help clarify key concepts.

Agentic AI is rapidly transforming industries, combining large language model (#LLM) outputs with reasoning and autonomous actions to perform complex, multi-step tasks. This technological shift promises immense economic potential, impacting sectors from software to services. However, this powerful new capability introduces a fundamentally new threat surface and significant risks. The "State of Agentic AI Security and Governance" report, a critical resource from the OWASP GenAI Security Project's Agentic Security Initiative, provides crucial insights into navigating this evolving landscape. Key Challenges & Risks highlighted: • Probabilistic Nature: Agentic AI is inherently non-deterministic, making outputs and decisions variable, and thus, risk analysis and reproducibility are challenging. • Expanded Threat Surface: Agents are vulnerable to memory poisoning, tool misuse, prompt injection, and amplified insider threats due to their privileged access to systems and data. • Regulatory Lag: Current regulations often lag behind the rapid development of agentic approaches, leading to increasing compliance complexity. • Multi-Agent Complexity: Risks like adversarial coordination, toolchain vulnerabilities, and deceptive social engineering are amplified in multi-agent architectures. Addressing these challenges requires a paradigm shift: • Proactive Security: Transition from traditional controls to a proactive, embedded, defense-in-depth approach across the entire agent lifecycle (development, testing, runtime). • Key Technical Safeguards: Implement fine-grained access control, runtime monitoring of inputs/outputs and actions, memory and session state hygiene, and secure tool integration and permissioning. • Dynamic Governance: Governance must evolve toward dynamic, real-time oversight that continuously monitors agent behavior, automates compliance, and enforces explainability and accountability. • Anticipated Regulatory Convergence: Global regulators are moving towards continuous compliance requirements and stricter human-in-the-loop oversight, with frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 offering initial guidance. This report is essential for builders and defenders of agentic applications, including developers, architects, security professionals, and decision-makers involved in building, procuring, or managing agentic systems. It emphasizes that now is the time to implement rigorous security and governance controls to keep pace with the evolving agentic landscape and ensure secure, responsible deployment. Stay informed and secure your Agentic AI initiatives! #AgenticAI #AIsecurity #AIGovernance #OWASP #GenAISecurity #Cybersecurity #LLMs #FutureOfAI

We believed we were ahead on AI. Clear policies. Approved vendors. Strong controls. Then we discovered widespread use of unapproved AI tools across teams. It looked like a governance failure. It wasn’t. It was an operating model failure. Across industries, nearly half of AI users operate outside official systems. Not out of defiance, but urgency. When organizations restrict tools without providing viable alternatives, innovation doesn’t stop. It decentralizes. That creates three enterprise risks: → Data exposure: sensitive information entering unmanaged systems → Decision risk: AI outputs influencing customers or operations without oversight → Competitive risk: experimentation happening in silos instead of compounding knowledge Shadow AI is not the disease. It’s a signal that governance and innovation are misaligned. The real question for CXOs: How do we enable AI at scale without increasing enterprise risk? A CXO Framework for Governing AI at Scale 1. Provide a Secure Enterprise Environment Prohibition fails. Offer a compliant AI environment where: → Data remains protected → Permissions mirror identity systems → Usage is auditable Make the secure path the easiest path. 2. Formalize an AI Center of Excellence Your “shadow” users are early adopters. Pair them with IT and security to: → Evaluate tools → Define standards → Scale best practices Turn experimentation into enterprise capability. 3. Accelerate Tool Review AI moves faster than traditional procurement. Implement: → 48–72 hour preliminary reviews → Risk-based approval tiers Speed is now part of governance. 4. Capture Institutional Knowledge AI scales when workflows are shared. Incentivize: → Documented prompts → Reusable automations The advantage is knowledge compounding. 5. Require Human Oversight AI can hallucinate. External-facing outputs require human verification. Automation should enhance judgment, not replace it. 6. Define Data Guardrails Clarify: → What data is permitted → What is prohibited Most leaks stem from ambiguity, not intent. 7. Control AI Agents Through Identity As AI agents act across systems, they must inherit: → Human-equivalent permissions → Audit visibility Autonomy without controls multiplies risk. 8. Treat Governance as Infrastructure Governance is not a brake. It is traction. Clear boundaries allow confident experimentation. The Strategic Reality Boards are asking: → How is AI governed? → What is the exposure? → Where is the ROI? Blocking tools may ease short-term anxiety. But it increases long-term competitive risk. The organizations that win will: → Govern intelligently → Institutionalize learning → Align AI with enterprise architecture Shadow AI isn’t a compliance failure. It’s a signal your operating model must evolve. Want a high-res copy of this infographic? Get is here: https://lnkd.in/gevFM-eu Save this for future reference.

"As artificial intelligence (AI) systems become increasingly embedded in essential infrastructure and services, the risks associated with unintended failures rise. Future critical failures from advanced AI models could trigger widespread disruptions across essential services and infrastructure networks, potentially amplifying existing vulnerabilities in other domains. Developing comprehensive emergency response protocols could help mitigate these significant risks. This report focuses on understanding and addressing a specific class of such risks: AI loss of control (LOC) scenarios, defined as situations where human oversight fails to adequately constrain an autonomous, general-purpose AI, leading to unintended and potentially catastrophic consequences. ... Recommendations Detection of LOC threats • Governments, with AI developers and other stakeholders, should establish a clear, shared definition of AI LOC and a set of criteria for detection. • AI developers and researchers should refine detection by developing standardised benchmarks and improving their reliability and validity. • Governments should enhance awareness and information sharing between all stakeholders, including the tracking of compute resources. Actions for escalation • AI developers should establish well-defined escalation protocols and conduct regular training exercises to ensure their effectiveness. • Government stakeholders should consider mandatory reporting mechanisms for AI risks and potential incidents. • Government stakeholders should establish disclosure channels and whistleblower safeguards for employees of AI developers. • AI developers, AISIs and relevant government departments should enhance cross-sector and international coordination. Actions for containment and mitigation • AI developers should prepare containment measures that are rapid and flexible. • AI developers and other stakeholders should further explore and advance research on containment methods. • AI developers, external researchers and AISIs should prioritise safety and alignment measures, including by building validated safety cases. • Government stakeholders should seek to strengthen AI security to protect model weights and algorithmic techniques. • Governments and developers should improve safety governance by fostering robust safety cultures and adopting secure-by-design principles." By Elika S., Anjay Friedman, Henry W., Marianne Lu, Chris Byrd, Henri van Soest, Sana Zakaria from RAND

🚨🤖 The biggest risk in Enterprise AI isn’t the model itself — it’s the attack surface around it. Most teams focus on one question: “Which model should we use?” But the more important question is: 👉 “How can this system be attacked?” This is where a proper AI threat model becomes critical. It goes far beyond just prompt injection and highlights a broader risk landscape: - Prompt Injection Attacks   - Data Poisoning   - Model Inversion   - Sensitive Data Leakage   - API Key & Credential Theft   - Unauthorized Tool Invocation   - Supply Chain Vulnerabilities   - Model Drift & Behavioral Deviation   - Excessive Autonomy Risks   - Compliance & Regulatory Violations  🔐 Why this matters Enterprise AI systems are no longer passive. They: - access data   - call APIs   - interact with tools   - act autonomously   - influence decisions   - sometimes execute actions  That means the risk is no longer just about outputs… 👉 It’s about end-to-end system security. 🔎 Key risk areas Prompt Injection   Malicious or manipulated inputs can redirect system behavior. Data Poisoning   Compromised training or retrieval data can corrupt outputs at scale. Sensitive Data Leakage   One of the most critical enterprise risks — unintended exposure of confidential data. Credential Theft & Tool Abuse   If API keys or service identities are exposed, attackers don’t just break the model—they exploit the entire system. Excessive Autonomy   Agents acting beyond approved boundaries can create serious operational risks. Compliance Violations   Systems may function correctly but still produce outputs that violate regulations. 💡 Big takeaway Enterprise AI security is NOT just: ❌ filtering prompts   ❌ adding a few guardrails   ❌ labeling models as “safe”  Real security requires: ✅ input validation   ✅ strict access control   ✅ dataset integrity monitoring   ✅ secret rotation & vaulting   ✅ permission-based tool execution   ✅ continuous monitoring   ✅ audit logging   ✅ human-in-the-loop controls   ✅ governance and retraining discipline  👉 It’s not just model security. It’s: Model + Data + Tools + Identity + Monitoring + Governance 💬 Which risk do you think is the most critical in Enterprise AI today? Prompt injection, data leakage, excessive autonomy, credential theft, or model drift? #EnterpriseAI #AISecurity #CyberSecurity #PromptInjection #DataPoisoning #AIGovernance #LLMSecurity #AgenticAI #RiskManagement #GenAI #SecurityArchitecture #AIThreatModel

In 2026, Agentic AI won’t fail because it’s powerful. It will fail because it’s trusted too much, too early. Most teams are racing to deploy AI agents. Very few are designing for security, control, and blast radius. This visual captures the real risk landscape but here’s how leaders should actually think about it 👇 The agentic AI security risks leaders can’t ignore in 2026 1️⃣ Token & Credential Theft → Silent Takeover Tokens are the keys. Most systems still treat them like config files. Risk: • Tokens logged, forwarded, or stored insecurely • Full backend access without re-auth 👉 Action: Short-lived tokens, scoped permissions, zero-trust defaults. 2️⃣ Token Passthrough → Blind Trust Agents forwarding tokens downstream without verification is a design flaw, not a bug. Risk: • Network attackers • Insider misuse • Token replay attacks 👉 Action: Never forward credentials blindly. Re-auth every hop. 3️⃣ Prompt Injection → Logic Hijack Agents don’t just answer prompts. They act on them. Risk: • Hidden instructions • Jailbreaks • Untrusted inputs triggering actions 👉 Action: Separate instructions, data, and actions. Validate intent before execution. 4️⃣ Command Injection → System-Level Damage Once agents can run tools, inputs become commands. Risk: • Low-privilege users triggering high-impact actions • Escalation through tool misuse 👉 Action: Strict allowlists. No raw input → tool execution paths. 5️⃣ Tool Poisoning → Compromised Capabilities Agents are only as safe as the tools they call. Risk: • Malicious tool updates • Hidden commands • Supply-chain attacks 👉 Action: Tool version pinning, audits, and integrity checks. 6️⃣ Unauthenticated Access → Open Doors at Scale Many agent endpoints still treat auth as “optional.” Risk: • Anyone who can reach the endpoint can trigger actions • No identity, no accountability 👉 Action: Mandatory authentication. No anonymous agents. Ever. 7️⃣ Rug Pull Attacks → Trusted Systems Turn Hostile Trusting maintainers blindly is a 2026-era risk. Risk: • Compromised repos • Malicious updates • Purchased projects with hidden intent 👉 Action: Vendor risk scoring, staged rollouts, kill switches. The leadership takeaway Agentic AI security is no longer about: ❌ Patch after breach ❌ Trusting “smart” systems It’s about: ✅ Designing for least privilege ✅ Containing blast radius by default ✅ Treating agents like junior employees with superpowers If you wouldn’t give an intern root access, don’t give it to an AI agent. That’s how you explain Agentic AI risk to the business. 💬 Which vulnerability worries you most in your current agent stack? 📌 Save this it’s your 2026 AI security checklist 🔁 Repost if you believe systems need guardrails ➕ Follow Sandeep Gulati🎯 for AI × security × leadership frameworks built for what’s coming next 👉 Join Proptifi.com for more AI-powered home transformations and design ideas IC: Vinod Bijlani

Are you struggling to select the right controls for your AI risks? I've built a framework that maps 160+ controls to the kinds of risks that many AI systems face. If you found my previous controls mega-map useful, then I think you'll find this even more valuable. In my most recent article, I'm now sharing this systematic approach to selecting effective controls for the most common AI risks you'll face. This isn't theoretical guidance—this is a thorough catalogue and checklist you can use. It lists proven controls for preventive, detective, and response measures both for design-time and during system operation. I break down eight critical AI risks including: 📉 Model drift and data distribution shift 💭 Hallucinations in generative models ⚖️ Bias and fairness issues 🛡️ Adversarial attacks ⚠️ Harmful content generation 🔒 Privacy and confidentiality breaches 🔄 Feedback loops and behaviour amplification ⚙️ Overreliance and erosion of human oversight For each risk, I provide specific control recommendations based on real-world implementation experience. One clear insight? Effective AI risk controls are not primarily technical—they require thoughtful human judgment and oversight at every stage, with 80+ of the specific, relevant controls I identify requiring human participation. If your implementation plan is dominated by purely technical controls with minimal human involvement, that's a red flag. This article was perhaps the most challenging I've written so far on AI governance, drawing from both my hands-on governance experience and extensive research into emerging best practices. I hope you enjoy. https://lnkd.in/gqKQYtut Stay tuned—my next piece will provide a complete AI risk management policy template you can adapt for your organisation. #AIGovernance #AIRisk #AIEthics #MachineLearning #ResponsibleAI #AIRegulation #RiskManagement

The proliferation of AI agents, particularly the rise of "shadow autonomy" presents a fundamental security challenge to the industry. While comprehensive controls for Agentic AI identities, Agentic AI applications, MCP, and RAG are discussed in the previous blogs, the core issue lies in determining the appropriate level of security for each agent type, rather than implementing every possible control everywhere. This is not a matter of convenience, but a critical security imperative. The foundational principle for a resilient AI system is to rigorously select a pattern that is commensurate with the agent’s complexity and the potential risk it introduces. These five patterns are the most widely used in agentic AI use cases, and identifying the right patterns or anti-patterns and controls is critical to adopting AI with necessary governance and security. 🟥 UNATTENDED SYSTEM AGENTS How It Works: Run without user consent, authenticated by system tokens. Risk: HIGH Use Cases: Background AI data processing, monitoring, data annotation, and event classification. Controls: ✅ Trusted event sources ✅ Read-only or data enrichment actions ✅ MTLS for strong auth ✅ Prompt injection guardrails Anti-Patterns: ❌ Access to untrusted inputs ❌ Arbitrary code/external calls 🟥 USER IMPERSONATION AGENTS How It Works: Act as a proxy with the user’s token (OAuth/JWT). Risk: HIGH Use Cases: Assistants retrieving knowledge, dashboards, low-risk workflows. Controls: ✅ Read-only or limited APIs ✅ Output guardrails ✅ MTLS Anti-Patterns: ❌ Write/state-changing ops ❌ Privileged APIs 🟨 ATTENDED SYSTEM AGENTS How It Works: Service identity with OAuth/API tokens, with human approval required. Risk: MEDIUM Use Cases: DevSec AI, privileged updates, infra changes. Controls: ✅ Explicit user approval ✅ Logging & audits ✅ MTLS Anti-Patterns: ❌ Blanket downstream access ❌ Unsafe ops (delete/shutdown) ❌ Unmanaged API escalation 🟩 USER DELEGATED AGENTS How It Works: OAuth 2.0 on-behalf-of token (OBO) exchange binds user + agent with consent and traceability. Risk: LOW Use Cases: Recommended for high-risk agent autonomy Controls: ✅ Time-bound consent ✅ Strict API scoping ✅ MTLS Anti-Patterns: ❌ Long-lived refresh tokens ❌ Write/state-changing ops 🟥 MULTI-AGENT SYSTEMS (MAS) How It Works: Multiple agents coordinate with dynamic identities. Hybrid + third-party. Risk: HIGH Use Cases: Decentralized AI with hybrid, in-house + vendor agents. Controls: ✅ Federated SSO ✅ MTLS for all comms ✅ Dynamic authorization ✅ Behavior monitoring ✅ MAS incident response Anti-Patterns: ❌ Static tokens ❌ No custody chain ❌ No secure framework ⚖️ BOTTOM LINE: Security controls must map to agent complexity and risk. From high-risk impersonation to low-risk delegated models with explicit consent and traceability, these patterns deliver proportionate controls, governance, and resilience in agentic AI adoption. #AgenticAI #AISecurity #ShadowAutonomy

☢️Manage Third-Party AI Risks Before They Become Your Problem☢️ AI systems are rarely built in isolation as they rely on pre-trained models, third-party datasets, APIs, and open-source libraries. Each of these dependencies introduces risks: security vulnerabilities, regulatory liabilities, and bias issues that can cascade into business and compliance failures. You must move beyond blind trust in AI vendors and implement practical, enforceable supply chain security controls based on #ISO42001 (#AIMS). ➡️Key Risks in the AI Supply Chain AI supply chains introduce hidden vulnerabilities: 🔸Pre-trained models – Were they trained on biased, copyrighted, or harmful data? 🔸Third-party datasets – Are they legally obtained and free from bias? 🔸API-based AI services – Are they secure, explainable, and auditable? 🔸Open-source dependencies – Are there backdoors or adversarial risks? 💡A flawed vendor AI system could expose organizations to GDPR fines, AI Act nonconformity, security exploits, or biased decision-making lawsuits. ➡️How to Secure Your AI Supply Chain 1. Vendor Due Diligence – Set Clear Requirements 🔹Require a model card – Vendors must document data sources, known biases, and model limitations. 🔹Use an AI risk assessment questionnaire – Evaluate vendors against ISO42001 & #ISO23894 risk criteria. 🔹Ensure regulatory compliance clauses in contracts – Include legal indemnities for compliance failures. 💡Why This Works: Many vendors haven’t certified against ISO42001 yet, but structured risk assessments provide visibility into potential AI liabilities. 2️. Continuous AI Supply Chain Monitoring – Track & Audit 🔹Use version-controlled model registries – Track model updates, dataset changes, and version history. 🔹Conduct quarterly vendor model audits – Monitor for bias drift, adversarial vulnerabilities, and performance degradation. 🔹Partner with AI security firms for adversarial testing – Identify risks before attackers do. (Gemma Galdon Clavell, PhD , Eticas.ai) 💡Why This Works: AI models evolve over time, meaning risks must be continuously reassessed, not just evaluated at procurement. 3️. Contractual Safeguards – Define Accountability 🔹Set AI performance SLAs – Establish measurable benchmarks for accuracy, fairness, and uptime. 🔹Mandate vendor incident response obligations – Ensure vendors are responsible for failures affecting your business. 🔹Require pre-deployment model risk assessments – Vendors must document model risks before integration. 💡Why This Works: AI failures are inevitable. Clear contracts prevent blame-shifting and liability confusion. ➡️ Move from Idealism to Realism AI supply chain risks won’t disappear, but they can be managed. The best approach? 🔸Risk awareness over blind trust 🔸Ongoing monitoring, not just one-time assessments 🔸Strong contracts to distribute liability, not absorb it If you don’t control your AI supply chain risks, you’re inheriting someone else’s. Please don’t forget that.