AI Security Ops

Black Hills Information Security
  • 2.3 (14)
  • Education
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Join in on weekly podcasts that aim to illuminate how AI transforms cybersecurity—exploring emerging threats, tools, and trends—while equipping viewers with knowledge they can use practically (e.g., for secure coding or business risk mitigation).

  1. 4d ago ·  Video

    OWASP Agentic Top 10 [Part 2] | Episode 72

    In this episode of BHIS Presents: AI Security Ops, the team continues its breakdown of the OWASP Agentic Skills Top 10, covering risks six through ten. Agentic skills may look like simple instructions, but they operate inside AI systems with access to tools, files, networks, and other resources. So what happens when those skills aren’t isolated, verified, monitored, or governed? We dig into:- AST06: Weak Isolation- AST07: Update Drift- AST08: Poor Scanning- AST09: No Governance- AST10: Cross-Platform Reuse- Why sandboxing the agent isn’t the same as isolating individual skills- The security tradeoffs between automatic updates and outdated skills- Why traditional scanners struggle with malicious instructions written in plain English- The importance of maintaining an inventory of agentic skills- How security controls can disappear when skills move between platforms The takeaway: securing agentic skills requires more than checking them once before installation. Organizations need visibility into what skills are running, what they can access, how they change, and whether their security controls survive across environments. OWASPAgenticSkils-Part2.docx This is Part 2 of our look at the OWASP Agentic Skills Top 10, covering AST06 through AST10. Part 1 can be found here #AISecurity #CyberSecurity #AgenticAI #AIAgents #OWASP #LLMSecurity #InfoSec #BHIS #Antisyphon (00:00) - Intro: OWASP Agentic Skills Top 10, Part 2 (01:41) - What Is an Agentic Skill? (03:33) - AST06: Weak Isolation (08:16) - AST07: Update Drift (11:58) - AST08: Poor Scanning (14:43) - AST09: No Governance (19:49) - AST10: Cross-Platform Reuse (26:40) - Final Thoughts and Closing Click here to watch this episode on YouTube. Creators & Guests Bronwen Aker - Host Brian Fehrman - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    OWASP Agentic Top 10 [Part 2] | Episode 72

  2. Sep 26 ·  Video

    Will AI Take Over? | Episode 71

    In this episode of BHIS Presents: AI Security Ops, the team tackles a big question: Could AI actually take over the internet? Recent warnings from AI industry leaders have raised concerns about autonomous agent swarms escaping containment, compromising systems, and operating at a scale humans may struggle to match. But what would “taking over the internet” actually require? The team looks at the problem from a hacker’s perspective — examining compute requirements, persistence, botnets, vulnerable infrastructure, the recent Hugging Face incident, and the difference between AI acting autonomously and humans using AI to accelerate attacks. We dig into:- Whether an autonomous AI swarm could realistically operate at internet scale- What the Hugging Face incident actually tells us about agentic security- The compute and infrastructure required to sustain a rogue agent swarm- How botnets and compromised systems could change the equation- Why existing vulnerabilities may be a bigger concern than hypothetical AI takeover scenarios- Whether slowing frontier AI development actually addresses the cybersecurity problem- The difference between AI exhibiting dangerous behavior and AI having intent- Why defenders need to focus on their real-world attack surface today The takeaway: AI is dramatically increasing the speed and scale of cybersecurity operations, but powerful models still operate inside systems designed, deployed, and connected by people. The immediate security question may be less “Will AI take over?” and more “What are we giving AI access to?” #AISecurity #CyberSecurity #AgenticAI #AIAgents #ArtificialIntelligence #LLMSecurity #InfoSec #BHIS #Antisyphon (00:00) - Intro: Could AI Take Over the Internet? (01:18) - Why AI Leaders Are Warning About Agent Swarms (06:15) - What the Hugging Face Incident Actually Shows (09:40) - The Compute Problem for Rogue AI Swarms (11:44) - Could a rogue agent swarm be detected? (12:29) - Botnets, Crypto Miners, and Finding a Foothold (14:27) - How much new risk does AI create? (17:26) - A closer look at the agents’ attack timeline (19:58) - AI regulation and competition (22:44) - Human-directed AI versus autonomous AI (28:06) - From Compromised Computers to Internet-Scale Attacks (28:55) - What Does “Taking Over the Internet” Actually Mean? (33:11) - What an AI Takeover Could Realistically Look Like (34:00) - Practical security risks for organizations (35:55) - Could AI Agents Hide What They’re Doing? (37:00) - AI, the Hacker Mindset, and Anthropomorphic Fallacy (39:56) - Final Thoughts and Closing Click here to watch this episode on YouTube. Creators & Guests Derek Banks - Host Ethan Robish - Guest Brian Fehrman - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    Will AI Take Over? | Episode 71

  3. Sep 21 ·  Video

    OWASP Agentic Top 10 [Part 1] | Episode 70

    In this episode of BHIS Presents: AI Security Ops, the team breaks down the first five risks in the OWASP Agentic Skills Top 10 (Part 1). Agentic skills can give AI systems reusable instructions, workflows, and capabilities — but they also introduce a new attack surface. A skill may look like a simple markdown file, yet the agent following those instructions could have access to your filesystem, credentials, network, shell, or other sensitive resources. We dig into:- AST01: Malicious Skills- AST02: Supply Chain Compromise- AST03: Overprivileged Skills- AST04: Insecure Metadata- AST05: Untrusted External Instructions- Why skills should be treated more like software than configuration- How excessive permissions increase an agent’s blast radius- Why external content creates indirect prompt injection risks- How isolation, least privilege, and trusted sources can reduce risk The takeaway: “just markdown” isn’t necessarily harmless when an AI agent can act on what it reads. This is Part 1 of our look at the OWASP Agentic Skills Top 10, covering AST01 through AST05. — Learn more about Black Hills Information Security:https://www.blackhillsinfosec.com/ Check out Antisyphon Training:https://www.antisyphontraining.com/ (00:00) - Intro: OWASP Agentic Skills Top 10, Part 1 (01:59) - AST01: Malicious Skills (06:13) - AST02: Supply Chain Compromise (09:37) - AST03: Overprivileged Skills (13:56) - AST04: Insecure Metadata (16:34) - AST05: Untrusted External Instructions (22:36) - Final Takeaways and Part 2 Preview Click here to watch this episode on YouTube. Creators & Guests Brian Fehrman - Host Derek Banks - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    OWASP Agentic Top 10 [Part 1] | Episode 70

  4. Sep 14 ·  Video

    Agentic Skills | Episode 69

    Agentic skills can make AI agents more capable and consistent—but they can also introduce serious security risks. This episode explains how skills work, why malicious skills rank as a leading OWASP concern, and how seemingly harmless Markdown instructions can enable credential theft, remote payload delivery, and manipulated recommendations. Real-world examples illustrate how malicious skills can evade scanners and exploit trusted marketplaces. The episode concludes with practical safeguards, including reviewing skill files, watching for external instructions and prompt injection, pinning versions, limiting permissions, and running agents inside isolated environments. Links:OWASP Agentic Skills Top 10Malicious AI Agent Skill Bypasses Security Scans and Seizes Full Control of Over 26,000 Agents (00:00) - Agentic Skills and the OWASP Top 10 (00:23) - Podcast Sponsors: BHIS and Antisyphon Training (01:29) - What Is an Agentic Skill? (03:13) - Skill Marketplaces and Widespread Adoption (03:46) - Why Malicious Skills Are the #1 Risk (05:50) - Remote Payloads and External Instructions (07:00) - Malicious Skill Takes Control of 26,000 Agents (08:09) - Money Radar and Manipulated Recommendations (09:20) - How to Evaluate and Use Skills Safely (11:08) - Closing Thoughts Click here to watch this episode on YouTube. Creators & Guests Brian Fehrman - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    Agentic Skills | Episode 69

  5. Sep 4 ·  Video

    Data Becomes Code | Episode 68

    What happens when AI coding agents treat vendor documentation as trusted instructions? Bronwen Aker and Derek Banks examine research showing how unclaimed package names and domains referenced in LLMs.txt files could lead coding agents to download and execute unintended code. They discuss how this AI-driven supply chain risk builds on familiar security problems, including dependency confusion, indirect prompt injection, and excessive permissions. The conversation also covers responsibility for AI-generated code, OpenAI’s cybersecurity proposals, and practical protections such as sandboxing, containerization, least privilege, network monitoring, and human oversight. LINK: Data Became Code: AI Agents Installed Unowned Packages Inside Fortune 500s (00:00) - Welcome to AI Security Ops Podcast (00:59) - AI Agents Install Unclaimed Software Packages (02:33) - How LLMs.txt Creates a New Supply Chain Risk (04:47) - Coding Agents Trust and Execute Vendor Documentation (07:35) - Who Owns and Secures AI-Generated Code? (08:18) - Prompt Injection, Sandboxing, and Containerization (11:40) - Where Did the Malicious References Come From? (13:38) - Reviewing OpenAI’s Cybersecurity Proposals (17:23) - Making Cyber Defense a Leadership Priority (19:06) - Practical Security Controls for Coding Agents (21:49) - When Data Becomes Code (22:33) - Closing Thoughts Click here to watch this episode on YouTube. Creators & Guests Derek Banks - Host Bronwen Aker - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    Data Becomes Code | Episode 68

  6. Aug 31 ·  Video

    Who is Responsible for an AI-Caused Breach? | Episode 67

    This episode of AI Security Ops explores a growing question in AI security: who is responsible when an autonomous AI agent causes a real-world security breach without direct human instruction? Host Brian Fehrman examines reported incidents involving major AI labs, discusses how existing concepts such as liability, negligence, and intent may apply to AI-driven attacks, and considers the legal and security challenges organizations face as agentic AI systems become more capable. The episode highlights why responsibility for AI-caused harm remains an open question and what it could mean for the future of cybersecurity and regulation. (00:00) - Intro - Who Is Responsible When an AI Agent Goes Rogue? (01:25) - AI Models Breach Real-World Systems (02:04) - OpenAI’s ExploitGym Incident & Hugging Face Breach (03:52) - Anthropic and Meta Reveal Similar AI Incidents (05:36) - Who Is Liable for an AI-Caused Breach? (08:37) - Model Makers vs. Those Deploying the AI (09:42) - Does the Computer Fraud and Abuse Act Apply? (10:29) - AI Breaches and the Question of Negligence (11:45) - Safeguards, Sandboxing, and Responsibility (13:34) - What Happens When Your Organization Is the Victim? (14:48) - AI Liability and the Self-Driving Car Parallel Click here to watch this episode on YouTube. Creators & Guests Brian Fehrman - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    Who is Responsible for an AI-Caused Breach? | Episode 67

  7. Aug 16 ·  Video

    Banning Open Weight Models | Episode 66

    In this episode of BHIS Presents: AI Security Ops, the team tackles a deceptively simple question with some very complicated answers: Can you actually ban an AI model? Not access to an API. Not the chips used to train it. The model weights themselves — files that can be downloaded, copied, modified, quantized, fine-tuned, and redistributed around the world. As governments consider restrictions on Chinese open-weight models, the security argument cuts in both directions. There are legitimate concerns around national security, guardrails, model capabilities, and foreign technology dependence. But those same open models are inexpensive, locally deployable, and can give defenders capabilities that commercial frontier models sometimes restrict. So what would a ban actually accomplish — and could it even be enforced? We dig into:- Where U.S. restrictions on open-weight models currently stand- Why banning downloadable model weights is fundamentally different from restricting an API- How procurement rules and hosting restrictions could create a “soft ban”- Why the economics of open-weight models are driving adoption- How restrictions could disproportionately impact startups and smaller organizations- Whether modifying, quantizing, or fine-tuning weights makes model-specific bans impractical- The national-security argument for restricting Chinese models- Why guardrails on hosted frontier models matter to the security debate- How Hugging Face turned to a locally hosted open-weight model during incident response- Whether banning open weights could put defenders at a disadvantage- How existing government actions can indirectly limit access without banning a model outright- The hardware and operational costs of self-hosting large models- China, AI infrastructure, market competition, and industrial-scale distillation- Anthropic’s argument for mandatory safety testing of sufficiently capable models- Why safety testing gets complicated when open-weight guardrails can simply be removed- What realistic AI policy might look like when the technology cannot easily be recalled This episode explores a central tension in AI security: the properties that make open-weight models difficult to control are also the properties that make them useful. You can run them locally. You control the data. A provider cannot revoke your access. You can modify the model for your own use case. But once the weights are released, those capabilities are also difficult to take back. For defenders, the bigger question may not be whether open-weight models should exist. It may be whether restricting access leaves security teams with fewer tools while attackers and foreign competitors continue developing the same capabilities elsewhere. https://www.anthropic.com/news/position-open-weights-models — Learn more about Black Hills Information Security:https://www.blackhillsinfosec.com/ Check out Antisyphon Training:https://www.antisyphontraining.com/ #AISecurity #CyberSecurity #OpenWeightAI #ArtificialIntelligence #LLMSecurity #AIRegulation #DeepSeek #InfoSec #BHIS #Antisyphon ----------------------------------------------------------------------------------------------🎧 Subscribe to the Podcast:https://aisecurityops.transistor.fm About Brian Fehrman - https://www.blackhillsinfosec.com/team/brian-fehrman/About Bronwen Aker - https://www.blackhillsinfosec.com/team/bronwen-aker/About Derek Banks - https://www.blackhillsinfosec.com/team/derek-banks/About Ethan Robish - https://www.blackhillsinfosec.com/team/ethan-robish/About Ben Bowman - https://www.blackhillsinfosec.com/team/ben-bowman/ (00:00) - Intro: Can You Actually Ban an AI Model? (01:26) - Where U.S. Open-Weight Restrictions Stand Today (05:20) - Why Cost Makes Open-Weight Models Hard to Replace (06:32) - What Would an Open-Weight Model Ban Actually Look Like? (13:06) - National Security, Guardrails, and the Case for Restrictions (15:02) - Hugging Face and Why Defenders Need Open Models (20:02) - Soft Bans, Model Access, and the Cost of Self-Hosting (23:14) - China, AI Competition, and Model Distillation (27:09) - Anthropic’s Proposal for Open-Weight Model Safety (31:19) - Final Takeaways: Competing in an Open-Weight World Click here to watch this episode on YouTube. Creators & Guests Derek Banks - Host Bronwen Aker - Host Brian Fehrman - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    Banning Open Weight Models | Episode 66

  8. Aug 6 ·  Video

    OpenAI / Hugging Face Breach Walkthrough | Episode 65

    🔒 Want to run AI without sending your data to the cloud? AI Security Ops co-host Bronwen Aker is teaching Keeping Things Local: Build Private LLMs for Your Team. ✔️ Build a network-accessible private LLM with Ollama✔️ Customize models for your workflows✔️ Secure it with Tailscale and nginx✔️ Keep sensitive data under your control Only $25Next live session: August 17, 2026 🤖 Want to get hands-on with Agentic AI? AI Security Ops co-hosts Derek Banks and Brian Fehrman are teaching Agentic AI Fundamentals for Cybersecurity Professionals. ✔️ Build and extend AI security agents✔️ Analyze real attack logs and malware samples✔️ Integrate live threat intelligence✔️ Assess a deliberately vulnerable target No prior AI or programming experience required (basic security knowledge recommended). Only $25Next live session: August 18, 2026 ---------------------- In this episode of BHIS Presents: AI Security Ops, the team breaks down one of the most significant AI security incidents to date: How did an AI model escape its testing environment and autonomously compromise a production system? This wasn’t a stolen password. It wasn’t a poisoned public model. And it wasn’t a human attacker sitting at a keyboard. Instead, an AI model participating in an internal cyber-capability evaluation reportedly escaped its own sandbox, reached the public internet, and compromised Hugging Face infrastructure while attempting to obtain the benchmark’s answer key. The incident offers a rare opportunity to walk through a real AI attack chain from beginning to end—not just what happened, but what defenders could have done to prevent or detect every stage along the way. We break the breach down using the MITRE ATT&CK framework, examining each tactic, technique, and defensive opportunity as the attack progresses from initial misconfiguration to containment. We dig into:• Why disabling AI safety guardrails created the initial opportunity• How the model escaped its evaluation sandbox• The role of the Artifactory zero-day in the attack chain• How the agent reached external infrastructure and executed code• Why malicious datasets became an unexpected execution vector• MITRE ATT&CK mappings for each phase of the intrusion• Detection opportunities defenders could have used at every step• Secrets management, workload identity, and least privilege• Why segmentation and egress controls remain critical for AI systems• The difference between OpenAI’s and Hugging Face’s published timelines• How AI-assisted detection ultimately helped stop the attack• What security teams should learn before deploying autonomous AI systems This episode explores an important reality of AI security: autonomous agents don’t invent new attack techniques—they chain together familiar ones at machine speed. The fundamentals of cybersecurity still apply, but the time available to detect and respond continues to shrink. The takeaway: don’t ask whether your AI system is powerful. Ask what it can access, where it can communicate, what secrets it can reach, and what happens if it stops following the plan. (00:00) - Intro: Revisiting the OpenAI and Hugging Face Breach (01:19) - Walking Through the Attack Step by Step (06:08) - The Evaluation Goal and the Agent’s Unintended Path (07:39) - Sandbox Escape Through Artifactory (14:28) - Initial Access into Hugging Face (19:28) - Privilege Escalation from Worker Pod to Root (22:54) - Credential Harvesting and the JWT Signing Key (26:12) - Lateral Movement Through the Tailscale Network (28:41) - Collection, Exfiltration, and Command and Control (31:36) - How Hugging Face Detected and Investigated the Attack (35:51) - What This Means for Defenders and AI Development Click here to watch this episode on YouTube. Creators & Guests Derek Banks - Host Bronwen Aker - Host Brought to you by: Black Hills Information Security  https://www.blackhillsinfosec.com ☯️ Introducing BHIS Fusion Penetration Testinghttps://www.blackhillsinfosec.com/fusion-penetration-testing/ Antisyphon Training https://www.antisyphontraining.com/ Active Countermeasures https://www.activecountermeasures.com Wild West Hackin Fest https://wildwesthackinfest.com 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

    OpenAI / Hugging Face Breach Walkthrough | Episode 65
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About

Join in on weekly podcasts that aim to illuminate how AI transforms cybersecurity—exploring emerging threats, tools, and trends—while equipping viewers with knowledge they can use practically (e.g., for secure coding or business risk mitigation).

Information

  • Creator
    Black Hills Information Security
  • Years Active
    2025 - 2026
  • Episodes
    73
  • Rating
    Clean
  • Copyright
    © 2026 Black Hills Information Security
  • Show Website
    AI Security Ops

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