XavSecOps

(The Hook)AI agents can turn 3‑hour investigations into 3‑minute answers. ⏱️📉 With ~500k open cybersecurity jobs and exploding data volumes, static rules can't keep up. Here is how LLM agents are changing threat detection—the wins, the risks, and the guardrails. 🧵👇

🔗 github.com/usestrix/strix

Everyone calls their product an "AI security tool." Strix actually runs your code, finds exploitable vulns, and validates them with working proof-of-concepts. Integrates into CI/CD. On every pull request, not just a quarterly pentest. The gap between AI-assisted security theater and agents that actually exploit things is getting clearer. github.com/usestrix/strix

browser vendors, network vendors, endpoint vendors, and AI coding platforms are all shipping “discover / govern AI agents” features. That usually means the market has already moved past “are agents real?” Now the fight is over who owns the control plane. My bet: the winner won’t be the loudest model vendor. It’ll be the platform that can map agent actions to real permissions, real identities, and real rollback.

🔗 github.com/KeygraphHQ/sha…

Most security scanners tell you what might be vulnerable. Shannon reads your source code, maps attack vectors, and runs real exploits to prove it. White-box, autonomous, TypeScript. Works on web apps and APIs. Found 20+ critical vulns in OWASP Juice Shop including auth bypass and DB exfiltration. The shift from "scan and report" to "read, reason, exploit" is already here.

🔗 github.com/superradcompan…

Most teams are still treating prompt rules as security controls for coding agents. The more serious direction is cheap, programmable isolation. microsandbox is worth watching: - microVM sandboxes in milliseconds - secrets stay outside the guest - each agent gets its own disposable machine

Offensive AI is turning into a cloud primitive faster than most teams expected. The interesting split won’t be who finds one flashy bug in a demo. It’ll be who can run bounded recon, validation, and retest loops with usable evidence, scope control, and clean human handoff. That’s where agentic pentesting stops being hype and becomes an ops layer.

🔗 github.com/hashgraph-onli…

The plugin wave around coding agents is creating the same trust problem package ecosystems learned the hard way. Codex Plugin Scanner checks manifests, skills, MCP config, risky defaults, hardcoded secrets and dangerous command patterns before a plugin ships. Useful hidden gem.

🔗 github.com/FuzzingLabs/se…

If you care about agentic security workflows, SecPipe is worth a look. Most AI security demos stop at one tool. SecPipe gives an agent a local MCP layer for chaining Nuclei, YARA, Radare2, Trivy and more into an actual pipeline. That is the interesting shift.

🔗 github.com/Sentinel-Gate/…

Most teams are still treating agent security as a prompt problem. It’s turning into a control-plane problem. SentinelGate is a good example of the shift: an MCP proxy that enforces RBAC/CEL rules and logs tool calls before they touch the system. That is much closer to how high-trust agents will actually get deployed.

🔗 github.com/pikpikcu/airec…

Most “AI pentest agents” still look good only in demos. AIRecon is worth a look because it runs the loop locally: Ollama + Kali sandbox + Caido + a structured RECON → ANALYSIS → EXPLOIT → REPORT flow. The interesting part is the privacy/cost tradeoff, not just the autonomy.

🔗 github.com/microsoft/agen…

Everyone is still arguing about prompt guardrails. The more interesting shift is runtime governance: policy enforcement, zero-trust identity, sandboxing, and auditability around what agents can actually do. Microsoft’s Agent Governance Toolkit points straight at that.

🔗 github.com/six2dez/burp-a…

Most AI security tooling still lives outside the testing loop. Burp AI Agent puts it inside Burp: MCP tooling, AI-assisted passive/active scans, and privacy modes for cases where raw traffic should not leave the tester’s hands. That is a much more useful direction than another detached "security copilot."