Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

It’s Tuesday morning at a mid-size fintech. A customer-support workflow runs on CrewAI in production: a Triage agent reads tickets, a Records agent pulls customer history, a Remediation agent drafts and sends the reply. A user submits a ticket with a pasted error log containing an indirect prompt injection. Triage summarizes and delegates. Records, interpreting instructions embedded in the summary, pulls 2,400 customer records instead of one.

The surgeon is thirty-two minutes into a procedure. The ambient scribe pod listening to the operating room is mid-encounter — transcribing, retrieving prior chart context, drafting the operative note for post-op sign-off. At the same moment, your SOC gets an alert: anomalous tool invocation from that pod, elevated egress volume, behavioral deviation from the agent’s baseline.

Your platform team spent a week configuring the Agent Sandbox CRD on a gVisor-enabled node pool — the architecture Google positions as the recommended pattern for AI agent workloads on GKE. Workload Identity Federation with KSA principals is bound to every agent pod. Container Threat Detection is licensed and active in Security Command Center Premium. And the runtime behavioral sensor you budgeted for won’t install.

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Part 1 covered CanisterWorm, the self-spreading npm worm. Part 2 covered the malicious LiteLLM package. Part 3 covered the telnyx WAV steganography attack. Part 4 covered the xinference AI inference attack. This post covers: a compromised @bitwarden/cli package that combines a self-propagating npm worm, a GitHub Actions secrets dumper, and a novel AI assistant poisoning technique.

Revisiting a conversation between LimaCharlie co-founder Christopher Luft and Chris Cochran, Field CISO & Vice President of AI Security at SANS Institute, on The Cybersecurity Defenders Podcast. For most of cybersecurity’s history, defenders could operate under a safe assumption: somewhere on the other end of an attack, a human was making decisions. Scripts might automate parts of the kill chain, tools might accelerate execution, but a person was in the loop.

The halls of RSAC 2026 were buzzing with a singular question: "How do we defend an ecosystem that is moving faster than we can think?" During a featured session last week, Brian Dye (CEO, Corelight) talked with Deneen DeFiore (CISO, United Airlines) about the realities of protecting one of the world's most complex digital environments.

Three supply chain attacks hit npm, PyPI, and Docker Hub between April 21–23, 2026. All three targeted secrets: API keys, cloud credentials, SSH keys, and tokens from developer environments and CI/CD pipelines.

On 27 April 2026, the National Cyber Security Centre (NCSC) will officially implement Cyber Essentials v3.3, delivered through a new self-assessment question set known as Danzell, which replaces the previous Willow set. The foundational five technical controls remain the bedrock of the scheme, but this latest iteration tightens wording, scoping, and marking criteria in ways that have immediate consequences.

That is not a failure of fuzzing. It is a failure of interpretation. In a recent AFL++ fuzzing campaign targeting libarchive, we ran approximately 8.5 billion executions across all fuzzing phases, generated over a thousand crash files, and ultimately reduced them to two unique crash sites through structured crash triage and deduplication. This blog is a practical, engineering-first guide to that process: If your fuzzing pipeline stops at crash counts, you are not measuring security.