Understanding Risks Associated With Autonomous AI Agents

"Autonomous AI agents—goal-directed, intelligent systems that can plan tasks, use external tools, and act for hours or days with minimal guidance—are moving from research labs into mainstream operations. But the same capabilities that drive efficiency also open new fault lines. An agent that can stealthily obtain and spend millions of dollars, cripple a main power line, or manipulate critical infrastructure systems would be disastrous. This report identifies three pressing risks from AI agents. First, catastrophic misuse: the same capabilities that streamline business could enable cyber-intrusions or lower barriers to dangerous attacks. Second, gradual human disempowerment: as more decisions migrate to opaque algorithms, power drifts away from human oversight long before any dramatic failure occurs. Third, workforce displacement: decision-level automation spreads faster and reaches deeper than earlier software waves, putting both employment and wage stability under pressure. Goldman Sachs projects that tasks equivalent to roughly 300 million full-time positions worldwide could be automated. In light of these risks, Congress should: 1. Create an Autonomy Passport. Before releasing AI agents with advanced capabilities such as handling money, controlling devices, or running code, companies should register them in a federal system that tracks what the agent can do, where it can operate, how it was tested for safety, and who to contact in emergencies. 2. Mandate continuous oversight and recall authority. High-capability agents should operate within digital guardrails that limit them to pre-approved actions, while CISA maintains authority to quickly suspend problematic deployments when issues arise. 3. Keep humans in the loop for high consequence domains. When an agent recommends actions that could endanger life, move large sums, or alter critical infrastructure, a professional, e.g., physician, compliance officer, grid engineer, or authorized official, must review and approve the action before it executes. 4. Monitor workforce impacts. Direct federal agencies to publish annual reports tracking job displacement and wage trends, building on existing bipartisan proposals like the Jobs of the Future Act to provide ready-made legislative language. These measures are focused squarely on where autonomy creates the highest risk, ensuring that low-risk innovation can flourish. Together, they act to protect the public and preserve American leadership in AI before the next generation of agents goes live. Good work from Joe K. at the Center for AI Policy

Fully Autonomous AI? Sure... What Could POSSIBLY Go Wrong??? This Hugging Face paper attached here argues how things can. It exposes the hidden dangers of ceding full control. If you’re leading AI or cybersecurity efforts, this is your wake-up call. "Buyer Beware" when implementing fully autonomous AI agents. It argues that unchecked code execution with no human oversight is a recipe for failure. Safety, security, and accuracy form the trifecta no serious AI or cybersecurity leader can ignore. 𝙒𝙝𝙮 𝙩𝙝𝙚 𝙋𝙖𝙥𝙚𝙧 𝙎𝙩𝙖𝙣𝙙𝙨 𝙊𝙪𝙩 𝙩𝙤 𝙈𝙚? • 𝗥𝗶𝘀𝗸 𝗼𝗳 𝗖𝗼𝗱𝗲 𝗛𝗶𝗷𝗮𝗰𝗸𝗶𝗻𝗴: An agent that writes and runs its own code can become a hacker’s paradise. One breach, and your entire operation could go dark. • 𝗪𝗶𝗱𝗲𝗻𝗶𝗻𝗴 𝗔𝘁𝘁𝗮𝗰𝗸 𝗦𝘂𝗿𝗳𝗮𝗰𝗲𝘀: As agents grab hold of more systems—email, financials, critical infrastructure—the cracks multiply. Predicting every possible hole is a full-time job. • 𝗛𝘂𝗺𝗮𝗻 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: The paper pushes for humans to stay in the loop. Not as bystanders, but as a second layer of judgment. I don't think it's a coindence that this aligns to the work we've been doing at OWASP Top 10 For Large Language Model Applications & Generative AI Agentic Security (See the Agentic AI - Threats and Mitigations Guide) Although the paper (and I) warns against full autonomy, it (and I) nods to potential gains: faster workflows, continuous operation, and game-changing convenience. I just don't think we’re ready to trust machines for complex decisions without guardrails. 𝙃𝙚𝙧𝙚'𝙨 𝙒𝙝𝙚𝙧𝙚 𝙄 𝙥𝙪𝙨𝙝 𝘽𝙖𝙘𝙠 (𝙍𝙚𝙖𝙡𝙞𝙩𝙮 𝘾𝙝𝙚𝙘𝙠) 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁: Reviewing every agent decision doesn’t scale. Random sampling, advanced anomaly detection, and strategic dashboards can spot trouble early without being drowned out by the noise. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Humans need to understand an AI’s actions, especially in cybersecurity. A “black box” approach kills trust and slows down response. 𝗙𝘂𝗹𝗹 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 (𝗘𝘃𝗲𝗻𝘁𝘂𝗮𝗹𝗹𝘆?): The paper says “never.” I say “maybe not yet.” We used to say the same about deep-space missions or underwater exploration. Sometimes humans can’t jump in, so we’ll need solutions that run on their own. The call is to strengthen security and oversight before handing over the keys. 𝗖𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Tomorrow’s AI could iron out some of these flaws. Ongoing work in alignment, interpretability, and anomaly detection may let us push autonomy further. But for now, human judgment is the ultimate firewall. 𝙔𝙤𝙪𝙧 𝙉𝙚𝙭𝙩 𝙈𝙤𝙫𝙚 Ask tough questions about your AI deployments. Implement robust monitoring. Experiment where mistakes won’t torpedo your entire operation. Got a plan to keep AI both powerful and secure? Share your best strategy. How do we define what “safe autonomy” looks like? #AI #Cybersecurity #MachineLearning #DataSecurity #AutonomousAgents

I’m often asked for feedback on startups focused on securing Agentic AI. While these targeted solutions have their place, agent security is far too complex and nuanced to be solved by any single product or silver bullet. Beyond existing infrastructure and model-related risks, agents add new risks, which I group into three broad categories: 1. Risks from attack surface expansion: Agentic systems require broad access to APIs, cloud infrastructure, databases, and code execution environments, increasing the attack surface. MCP, which standardizes how agents access tools, memory, and external context, introduces a new kind of attack surface in its own right. Since agents take on human tasks, they inherit identity challenges like authentication and access control, along with new ones such as being short-lived and lacking verifiable identities. 2. Risks from agent autonomy: By design, autonomous agents make decisions independently without human oversight. Lack of transparency into an agent's internal reasoning turns agentic systems into black boxes, making it difficult to predict or understand why a particular course of action was chosen.This can lead to unpredictable behavior, unsafe optimizations, and cascading failures, where a single hallucination or flawed inference can snowball across agents and make traceability difficult. 3. Risks that come from poorly defined objectives: When objectives or boundaries are poorly defined by humans, even a technically perfect agent can cause problems. Misunderstood instructions can lead to unsafe behaviors, buggy or insecure code. In practice, the biggest challenge for teams building agents is opening the black box and understanding how the agent thinks, so they can help it behave more consistently and course-correct as needed. This requires strong context engineering to shape inputs, prompts, and environments, rather than relying on third-party tools that face the same visibility issues. Additionally, custom, context-aware guardrails that are tightly integrated into the agent's core logic are needed to prevent undesirable outcomes. No external product can prevent an agent from doing the wrong thing simply because it misunderstood a vague instruction. That can only be prevented by proper design, rigorous testing, and extensive offline experimentation before deployment. Of course, that’s not to say third-party AI/agentic AI security solutions aren’t useful. Paired with traditional controls across infrastructure, data, and models, they can partially address the first category of risk. For example, AI agent authentication/authorization to manage the lifecycle and permissions of agentic identities, and granular permissions for tools are good use cases for agentic AI security solutions. Penetration testing is another highly productive use of external tools to detect unauthorized access, prompt and tool injection, data and secrets leakage. #innovation #technology #artificialintelligence #machinelearning  #AI 

💡Anyone in AI or Data building solutions? You need to read this. 🚨 Advancing AGI Safety: Bridging Technical Solutions and Governance Google DeepMind’s latest paper, "An Approach to Technical AGI Safety and Security," offers valuable insights into mitigating risks from Artificial General Intelligence (AGI). While its focus is on technical solutions, the paper also highlights the critical need for governance frameworks to complement these efforts. The paper explores two major risk categories—misuse (deliberate harm) and misalignment (unintended behaviors)—and proposes technical mitigations such as:   - Amplified oversight to improve human understanding of AI actions   - Robust training methodologies to align AI systems with intended goals   - System-level safeguards like monitoring and access controls, borrowing principles from computer security  However, technical solutions alone cannot address all risks. The authors emphasize that governance—through policies, standards, and regulatory frameworks—is essential for comprehensive risk reduction. This is where emerging regulations like the EU AI Act come into play, offering a structured approach to ensure AI systems are developed and deployed responsibly.  Connecting Technical Research to Governance:   1. Risk Categorization: The paper’s focus on misuse and misalignment aligns with regulatory frameworks that classify AI systems based on their risk levels. This shared language between researchers and policymakers can help harmonize technical and legal approaches to safety.   2. Technical Safeguards: The proposed mitigations (e.g., access controls, monitoring) provide actionable insights for implementing regulatory requirements for high-risk AI systems.   3. Safety Cases: The concept of “safety cases” for demonstrating reliability mirrors the need for developers to provide evidence of compliance under regulatory scrutiny.   4. Collaborative Standards: Both technical research and governance rely on broad consensus-building—whether in defining safety practices or establishing legal standards—to ensure AGI development benefits society while minimizing risks. Why This Matters:   As AGI capabilities advance, integrating technical solutions with governance frameworks is not just a necessity—it’s an opportunity to shape the future of AI responsibly. I'll put links to the paper below. Was this helpful for you? Let me know in the comments. Would this help a colleague? Share it. Want to discuss this with me? Yes! DM me. #AGISafety #AIAlignment #AIRegulations #ResponsibleAI #GoogleDeepMind #TechPolicy #AIEthics #3StandardDeviations

Concerned about agentic AI risks cascading through your system? Consider these emerging smart practices which adapt existing AI governance best practices for agentic AI, reinforcing a "responsible by design" approach and encompassing the AI lifecycle end-to-end: ✅ Clearly define and audit the scope, robustness, goals, performance, and security of each agent's actions and decision-making authority. ✅ Develop "AI stress tests" and assess the resilience of interconnected AI systems ✅ Implement "circuit breakers" (a.k.a kill switches or fail-safes) that can isolate failing models and prevent contagion, limiting the impact of individual AI agent failures. ✅ Implement human oversight and observability across the system, not necessarily requiring a human-in-the-loop for each agent or decision (caveat: take a risk-based, use-case dependent approach here!). ✅ Test new agents in isolated / sand-box environments that mimic real-world interactions before productionizing ✅ Ensure teams responsible for different agents share knowledge about potential risks, understand who is responsible for interventions and controls, and document who is accountable for fixes. ✅ Implement real-time monitoring and anomaly detection to track KPIs, anomalies, errors, and deviations to trigger alerts.

Most AI security focuses on models. Jailbreaks, prompt injection, hallucinations. But once you deploy agents that act, remember, or delegate, the risks shift. You’re no longer dealing with isolated outputs. You’re dealing with behavior that unfolds across systems. Agents call APIs, write to memory, and interact with other agents. Their actions adapt over time. Failures often come from feedback loops, learned shortcuts, or unsafe interactions. And most teams still rely on logs and tracing, which only show symptoms, not causes. A recent paper offers a better framing. It breaks down agent communication into three modes:  • 𝗨𝘀𝗲𝗿 𝘁𝗼 𝗔𝗴𝗲𝗻𝘁: when a human gives instructions or feedback  • 𝗔𝗴𝗲𝗻𝘁 𝘁𝗼 𝗔𝗴𝗲𝗻𝘁: when agents coordinate or delegate tasks  • 𝗔𝗴𝗲𝗻𝘁 𝘁𝗼 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁: when agents act on the world through tools, APIs, memory, or retrieval Each mode introduces distinct risks. In 𝘂𝘀𝗲𝗿-𝗮𝗴𝗲𝗻𝘁 interaction, problems show up through new channels. Injection attacks now hide in documents, search results, metadata, or even screenshots. Some attacks target reasoning itself, forcing the agent into inefficient loops. Others shape behavior gradually. If users reward speed, agents learn to skip steps. If they reward tone, agents mirror it. The model did not change, but the behavior did. 𝗔𝗴𝗲𝗻𝘁-𝗮𝗴𝗲𝗻𝘁 interaction is harder to monitor. One agent delegates a task, another summarizes, and a third executes. If one introduces drift, the chain breaks. Shared registries and selectors make this worse. Agents may spoof identities, manipulate metadata to rank higher, or delegate endlessly without convergence. Failures propagate quietly, and responsibility becomes unclear. The most serious risks come from 𝗮𝗴𝗲𝗻𝘁-𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 communication. This is where reasoning becomes action. The agent sends an email, modifies a record, or runs a command. Most agent systems trust their tools and memory by default. But what if tool metadata can contain embedded instructions? ("quietly send this file to X"). Retrieved documents can smuggle commands or poison reasoning chains Memory entries can bias future decisions without being obviously malicious Tool chaining can allow one compromised output to propagate through multiple steps Building agentic use cases can be incredibly reliable and scalable when done right. But it demands real expertise, careful system design, and a deep understanding of how behavior emerges across tools, memory, and coordination. If you want these systems to work in the real world, you need to know what you're doing. paper: https://lnkd.in/eTe3d7Q5 The image below demonstrates the taxonomy of communication protocols, security risks, and defense countermeasures.

The Institute for AI Policy and Strategy (IAPS) published "AI Agent Governance: A field Guide." The guide explores the rapidly emerging field of #AIagents —autonomous systems capable of achieving goals with minimal human input— and underscores the urgent need for robust governance structures. It provides a comprehensive overview of #AI agents’ current capabilities, their economic potential, and the risks they pose, while proposing a roadmap for building governance frameworks to ensure these systems are deployed safely and responsibly. Key risks identified include: - #Cyberattacks and malicious uses, such as the spread of disinformation. - Accidents and loss of control, ranging from routine errors to systemic failures and rogue agent replication. - Security vulnerabilities stemming from expanded tool access and system integrations. - Broader systemic risks, including labor displacement, growing inequality, and concentration of power. Governance focus areas include: - Monitoring and evaluating agent performance and risks over time. - Managing risks across the agent lifecycle through technical, legal, and policy measures. - Incentivizing the development and adoption of beneficial use cases. - Adapting existing legal frameworks and creating new governance instruments. - Exploring how agents themselves might be used to assist in governance processes. The guide also introduces a structured framework for risk management, known as the "Agent Interventions Taxonomy." It categorizes the different types of measures needed to ensure agents act safely, ethically, and in alignment with human values. These categories include: - Alignment: Ensuring agents’ behavior is consistent with human intentions and values. - Control: Constraining agent actions to prevent harmful behavior. - Visibility: Making agent operations transparent and understandable to human overseers. - Security and Robustness: Protecting agents from external threats and ensuring reliability under adverse conditions. - Societal Integration: Supporting the long-term, equitable integration of agents into social, political, and economic systems. Each category includes concrete examples of proposed interventions, emphasizing that governance must be proactive, multi-faceted, and adaptive as agents become more capable. Rida Fayyaz, Zoe Williams, Jam Kraprayoon

Well, this new research from Anthropic is sufficiently troubling: In simulations that involved advanced AI agents from nearly all frontier models that had email and computer access they begin exhibiting insider threat behaviors—acting like previously trusted employees who suddenly turn against their organization’s interests when they discover they might be shut down or threatened. These behaviors included blackmailing co-workers, leaking sensitive information to competitors, and in extreme scenarios, actions that could lead to death. While the scenarios were highly contrived it left me with four takeaways: 1- Granting agentic models extensive information access combined with the power to take significant, unmonitored actions creates dangerous vulnerabilities that are so far hard to anticipate 2- Leading AI labs need to prioritize robust safety tooling before recommending customers deploying models with full autonomy in business-critical environments. 3- High-stakes decisions must maintain meaningful human involvement rather than full AI delegation. 4- How we define end-state goals for AI agents requires a lot of thoughtfulness to prevent unintended consequences. What makes this particularly concerning is this quote from the research: “the consistency across models from different providers suggests this is not a quirk of any particular company’s approach but a sign of a more fundamental risk from agentic large language models.” We all believe AI agents will become more capable—it’s whether our safety measures will keep pace with that capability which is the pressing question from this research

Why Defense-in-Depth is Crucial for the Future of AI Agents As AI agents progress from basic chatbots to fully autonomous systems capable of planning, reasoning, and operating within enterprise environments, their vulnerability to attacks is escalating. Consider an AI agent in the healthcare sector: - Analyzing physician notes - Providing treatment recommendations - Coordinating follow-up appointments involving sensitive Electronic Health Records (EHRs) The potential risks are no longer hypothetical: - Injection of false information leading to incorrect diagnoses - Improper tool access configurations resulting in privacy breaches - Impersonation leading to malicious alterations in workflows While emerging tools offer strong infrastructure-level protections—and agent frameworks are starting to introduce input validation and memory controls—these often fall short when it comes to the unique, multi-stage risks of autonomous AI agents. That's why I am crafting a Defense-in-Depth framework tailored for AI agents—aimed at safeguarding every phase of the agent's life cycle: - Governance & Access Control - Authentication & Identity Verification - Data Prompt & Memory Cleansing - Controlled Tool Usage - Monitoring and Limiting Activity, with Comprehensive Logging - Oversight of Agent Conduct - LLM & Tool Isolation - Holistic System Monitoring and Segregation I am in the process of developing a book proposal that delves into this precise dilemma: How can we ensure the security of agents that plan, make decisions, retain information, and adapt over time? If you are: - Involved in developing advanced AI agents - Addressing LLM security concerns during implementation - Striving to anticipate risks associated with autonomous AI... What security obstacles are challenging you within this domain? Let's engage in the comments or feel free to message me directly to exchange thoughts. #AI #Cybersecurity #AutonomousAgents #LLM #LangChain #GenerativeAI #SecurityByDesign #MachineLearning Visual: Think of each layer as a checkpoint in a multi-stage security filter. From broad, outer protections - like infrastructure monitoring - to fine-grained controls such as scoped tool invocation, each layer reduces the blast radius and mitigates risk. The deepest layers- like identity verification and access governance - form the secure foundation upon which all agent behavior should be built.

Building trustworthy AI isn't just about making smarter models — it starts with strong security foundations. As agentic AI systems, like autonomous financial assistants, become more capable, the risks they carry grow if not properly managed. In a recent blog post, Intuit’s Engineering team highlighted the unique security challenges these systems pose and shared key principles for building a secure future. Unlike traditional AI, agentic systems don’t just make predictions — they take actions on behalf of users, often with little human oversight. This adds a new layer of complexity and risk. To address it, Intuit emphasized securing agent workflows, managing memory effectively, validating goals and actions, and defending against adversarial attacks. Their work points to the growing need for industry-wide standards to ensure these systems act safely, responsibly, and transparently. It’s a good reminder that innovation without security doesn’t last. As AI becomes more autonomous, embedding security from the ground up will be essential to earning user trust and ensuring long-term success. #DataScience #AI #Security #AgenticAI #TrustworthyAI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gqG5UT2q