23 posts tagged with “security”
The 12-factor-agents spec never says security out loud — yet owning your context window is DLP, a structured tool schema is your authorization point, and a human-contact tool call is your approval gate. Reliability and security are the same seams read twice.
Everyone is auditing the model. The real attack surface is the harness you wrap around it — the scaffolding that wires tools, memory, and control flow into an agent that can actually act.
We automated security alert triage to a 97% noise-reduction rate — then a suppression rule went stale and nearly let a production vulnerability through unnoticed.
An automated audit gave us nine recommendations for being 'agent-ready.' We shipped three and deliberately failed the other six — because a security firm's agent-readiness is measured by signal honesty, not checkbox coverage.
A penetration testing firm audited 15 applications built with AI coding assistants. They found 69 exploitable vulnerabilities, 6 critical. The estimated remediation cost: $1.5 million. Teams shipping AI-generated code need to focus on the security debt accumulating underneath.
Nearly 9 in 10 organizations report AI agent security incidents. The root cause isn't prompt injection or model flaws — it's overly broad permissions.
Static AI guardrails are failing in production. Langflow was exploited within 20 hours. Cline was compromised through a GitHub issue title. Here's what actually works instead.
NIST RA-5, ISO 27001 9.2, DORA, FedRAMP 20x — four major compliance frameworks share the same blind spot: none of them account for AI agents in your environment. Here is what that means and what to do about it.
820 malicious packages. 30,000 exposed instances. Fortune 500 breaches. The AI agent ecosystem has a supply chain problem that traditional AppSec isn't built to catch.
Everyone optimizes the token window. Almost nobody manages the environment. Active context is what your agent thinks about. Latent context is what your agent can reach. The blast radius of a compromised agent is determined by the latter.
Indirect prompt injection has moved from theory to active exploitation. Unit 42 confirms in-the-wild attacks, PleaseFix hijacks AI agents through calendar invites, and a Claude Code CVE exposed 150,000 developers. Here is what security teams need to know.
AI slop killed curl's seven-year bug bounty program. Then AI-powered research found 170 real bugs in the same codebase. The difference was not the technology. It was the methodology. What the curl saga teaches every security team about the gap between AI noise and AI discovery.
Nineteen malicious npm packages. Four AI coding tools. Rogue MCP servers injected silently into agent configurations. SANDWORM_MODE is the first documented autonomous supply chain attack targeting AI developer toolchains — and it exposes a structural vulnerability that identity alone cannot fix.
The upstream fix for CVE-2026-21852 protects interactive users. It does not protect headless mode. We tested this against our own production deployment and watched 18 API requests redirect to an attacker-controlled server in 30 seconds. Here is what we found and how to fix it.
Your AI agents inherit your permissions, your credentials, and your blast radius. NIST just proposed a fix — and the public comment window closes April 2. Here's why identity governance is the layer most agent architectures are missing.
Most teams treat token spend limits as cost management. They are blast radius containment. An autonomous agent with no spending ceiling is not a productivity tool — it is an uncontrolled liability.
Traditional SAST, DAST, and SCA tools were built for request-response architectures. Agent-first systems have vulnerability classes these tools were never designed to detect — and independent research just confirmed it.
Ambiguous specifications aren't just a project management problem anymore. In agent-first architectures, every gap in a spec is a potential security boundary violation — and the agent won't tell you it's guessing.
OWASP released its first Top 10 for Agentic Applications. Here's what each risk means, why traditional AppSec frameworks fall short, and how to start securing your AI agents today.
What a security engagement with Atypical Tech actually looks like — from the first call to the final deliverable. No mystery, no overhead, no surprises.
Every tool an agent can call is an attack surface. In agent-first architectures, the integration layer is the primary security boundary — and most teams aren't treating it that way.
How the Safe Autonomy framework applies to vulnerability triage, alert correlation, compliance evidence, and security testing. AI agents can multiply your security team—if you build the right guardrails.
Right-sized security for seed-to-Series-B. What actually matters, what can wait, and the mental model that scales.