Prompt injection is no longer the main risk; autonomous agents now need policy checks, tool allowlists, and human approval at runtime to prevent silent data leaks and destructive actions. If your AI can browse, write, or act, how do you stop it from chaining a poisoned prompt into a real-world incident?
The Anthropic incident made one thing clear: AI is no longer just helping defenders, it’s becoming part of the attack surface. If models can be probed, manipulated, or misused at scale, what security controls actually hold up?
Retrieval-augmented apps are now a top AI attack surface because poisoned documents can steer model answers, leak secrets, or trigger unsafe actions. This post shows the controls teams are using to sanitize sources, isolate tools, and verify retrieved context before generation.
Darktrace’s latest threat report says nearly 70% of incidents in the Americas now begin with stolen or misused accounts, not software exploits. As attackers use AI to move faster and adapt in real time, are traditional detection tools becoming too slow to catch the breach?
As more teams ship LLM-powered products, red teaming is shifting from a one-time test to a recurring control that finds prompt injection, data leakage, and unsafe tool use before attackers do. The question is no longer whether to test your model, but how to do it continuously without slowing delivery.
As LLM apps move from pilots to production, prompt injection is turning AI gateways into a practical control point for filtering malicious inputs, enforcing policy, and logging risky model calls. The real question is no longer whether to deploy one, but how to make it effective without breaking useful workflows.
Retrieval-augmented generation can leak secrets, amplify prompt injection, and surface poisoned documents if its data pipeline is not hardened end to end. This post shows the security controls practitioners need before RAG becomes their next production incident.
Prompt injection remains one of the most reliable ways to steer AI assistants into leaking data or taking unsafe actions, even when basic filters are in place. Learn why defenders are shifting from prompt-only controls to model isolation, tool permissioning, and runtime policy enforcement.
Prompt injection, data exfiltration, and tool misuse are no longer edge cases—they’re the failure modes security teams are finding first in production copilots and agentic systems. This post examines how AI red teaming catches these risks before attackers do, and which tests matter most in 2026.
LLM agents are moving from demos into production, but prompt filters alone won't stop unsafe tool calls or data exfiltration. This post explains how runtime policy enforcement can constrain agent actions without breaking useful automation.
Prompt injection is now a practical attack path against LLM apps, agents, and RAG systems—not just a research curiosity. This article shows how teams can test for it, harden tool use, and measure whether guardrails actually block malicious instructions.
As AI agents move from demos to production workflows, prompt injection remains the easiest way to turn a helpful model into a data-leaking one. This post breaks down which defenses—sandboxing, tool अनुमति gating, and output validation—actually reduce risk, and where teams still overtrust them.
Prompt injection is no longer just a chat app problem; it is a control-plane risk for AI agents that can browse, call tools, and act on data. This post shows how policy-grounded agents reduce unsafe actions without killing useful autonomy.
Prompt injection is still one of the easiest ways to make LLM apps leak data or ignore policy, especially when tools, retrieval, and memory are wired together. This post shows what modern AI red teaming needs to test—and why static guardrails fail in real deployments.
As AI agents start handling tickets, code, and customer data, red teaming is shifting from a one-off evaluation to a repeatable control for catching prompt injection, data leakage, and unsafe tool use before production. The real question is whether your AI system can survive an attacker who treats prompts, tools, and memory as one attack surface.
Prompt injection is no longer just a chatbot problem—it can poison retrieval pipelines, leak sensitive context, and steer downstream actions. This post examines practical defenses for securing RAG systems before attackers turn your vector store into an attack path.
As more organizations deploy copilots and RAG apps, prompt injection and data exfiltration have become operational risks, not edge cases. This post asks whether your current testing covers the attack paths that modern AI systems actually expose.
Watermarking is becoming a practical control for identifying text, images, and audio produced by generative AI—but attackers are already testing ways around it. The real question is whether defenders can deploy watermark checks fast enough to flag suspicious content before phishing campaigns, deepfakes, and fraud messages go viral.
Attackers are moving past simple prompt injection and exploiting retrieval, tool calls, and memory to steer LLM apps. This post shows why AI security teams now need retrieval-level controls, policy checks, and continuous red-teaming to keep RAG systems safe.
Prompt filtering alone is failing against indirect injection and tool-abuse attacks in agentic systems. Learn how runtime policy enforcement can block risky actions without breaking legitimate LLM workflows.
Prompt injection, data exfiltration, and tool misuse are no longer edge cases—they’re the failure modes security teams are finding in real LLM rollouts. This piece breaks down the AI red-teaming techniques practitioners are using to catch them before they hit production.
Prompt injection is still the fastest way to turn a helpful assistant into a data exfiltration path, especially when agents can read files, call tools, or browse the web. This post shows the concrete guardrails teams should deploy now—input isolation, tool अनुमति controls, output filtering, and runtime monitoring.
As AI agents move from demos to production, prompt injection is becoming a supply-chain problem, not just a chat bug. Learn how model context firewalls, tool अनुमति controls, and output filtering can block data exfiltration before an agent follows a malicious instruction.
AI agents are starting to call APIs, query databases, and trigger workflows—often with far more access than they need. Learn how least-privilege design, scoped tokens, and tool sandboxing can stop prompt injection from turning an assistant into an attack path.
LLM products can’t treat compliance as an add-on: GDPR may demand meaningful explanations for automated decisions, HIPAA can make prompts containing PHI a regulated data flow, and SOC 2 now has to cover model access, logging, and vendor risk. The hard question is whether your AI system can prove it handles sensitive data safely—even when the model itself is a black box.
AI now writes spear-phishing that looks tailored, timely, and almost indistinguishable from real internal mail, which is why legacy email filters are missing attacks that exploit context instead of keywords. This post shows what behavioral analysis and LLM-based detection can catch—and where human defenders still outperform the model.
An autonomous AI agent is only as safe as its weakest surface: the model, tools, memory, messages, and the human interface each create distinct paths for prompt injection, data exfiltration, and unauthorized action. This post maps those attack vectors end to end—and shows where defenders should place controls before the agent acts on its own.
Every AI SaaS app can quietly become a supply-chain risk if it sees your prompts, files, or customer data. Does your vendor questionnaire cover data processing agreements, model-training opt-outs, breach-notification SLAs, and the full subprocessor chain?
If an AI agent can fetch your data, can you prove which user, model, or workflow authorized it—and revoke that authority instantly? This post compares wallet-based identity, short-lived JWT delegation, and MCP session tokens, and shows why bearer tokens alone can’t answer the attribution problem.
A production LLM stack should log prompts, responses, model/version metadata, latency, token usage, refusals, and safety events so teams can detect drift, prompt injection, and cost spikes before users do. This post compares where Langfuse, Helicone, and Arize fit in the pipeline—and which signals each one surfaces best for alerting and anomaly detection.
When an AI system is compromised, the first question is no longer just “what data was stolen?”—it’s “what model behavior was altered, and where did it spread?” This piece maps the missing IR steps for model integrity checks, prompt-log forensics, and training-data contamination before the next incident becomes an organizational blind spot.
A single poisoned dataset can plant a hidden backdoor, flip labels at scale, or shift the feature space just enough to make a model fail only when it matters. This post shows the detection signals and monitoring controls that can catch contamination before a training run turns hostile.
AI APIs are being scraped, overused, and resold faster than many teams can notice, and the wrong auth choice can make every call a costly liability. This piece compares API keys, JWTs, and OAuth, then shows how to rate-limit and spot abuse without punishing legitimate users.
SOC teams are being promised fewer alerts, faster investigations, and less burnout—but which AI features are actually cutting time to triage, correlating logs reliably, and accelerating threat hunts? This post separates measurable ROI from common failure modes like false confidence, noisy automation, and hallucinated context.
Attackers are already using AI to mass-generate convincing phishing lures, mutate payloads between campaigns, and speed up vulnerability discovery—turning low-skill operators into far more effective threats. The hard question for defenders: when malware can rewrite itself and its social engineering in real time, which detections still work?
NIST’s AI Risk Management Framework is easier to apply when you treat it as four operational questions: who owns the model, what can go wrong, how do you prove it’s behaving, and how do you respond when it doesn’t? For a deployed LLM, "Measure" means more than accuracy—it means tracking jailbreak success rates, hallucination frequency, policy violations, latency, drift, and abuse signals against real production traffic.
When employees paste contracts, customer records, or source code into AI tools, vendors may retain prompts, outputs, and metadata far longer than your team expects—and opt-outs from training rarely stop all retention. This post explains what GDPR and CCPA actually require, how to verify a vendor’s data-use controls, and the deployment steps that reduce exposure before your next AI rollout.
AI agents can now automate recon, suggest exploit paths, and even chain steps with alarming speed—but they still struggle with context, novel defenses, and anything that requires real-world judgment. This post asks where PentestGPT, AutoAttacker, and similar tools are actually useful, and where ethics and authorization must draw a hard line.
A hallucinated answer is more than embarrassing when it tells an engineer to patch the wrong service, cites a fabricated CVE, or gives false confidence that a system is safe. This post breaks down the failure modes and the guardrails that can keep AI from turning bad security advice into real risk.
If employees are already pasting sensitive data into AI tools, what is your governance model doing to stop it? CISOs need a practical framework now: classify inputs, codify acceptable use, rehearse AI-specific incident response, and vet AI vendors before a breach starts with a prompt.
An attacker can turn an LLM into an exfiltration relay by hiding secrets in generated text patterns or by forcing tool calls that send data out through webhooks. This post shows the attack patterns, the telemetry that exposes them, and the controls that block leakage before the model becomes a silent data hose.
A structured red team should test four things in order: threat model, adversarial prompts, tool-abuse paths, and output validation gaps. This post shows a repeatable methodology for finding the failure modes attackers are most likely to exploit in 2026.
When the “user” is an AI agent, zero trust means every prompt, tool call, and data request must be verified, scoped, and logged in real time. This post shows how microsegmentation, just-in-time privilege, continuous identity checks, and tamper-evident audit trails stop agents from becoming an enterprise-wide blast radius.
A HuggingFace model can be more dangerous than it looks: malicious weights, unsafe deserialization (like PyTorch pickle CVEs), and tampered LoRA adapters can all turn a download into code execution or silent backdoors. The real question is: how do you verify provenance before an AI model reaches production?
A single malicious document in your vector store can steer answers, leak hidden instructions, or even exfiltrate sensitive data through a carefully crafted query. This post breaks down where RAG breaks first—and the concrete controls that stop poisoned retrieval, indirect prompt injection, and unauthorized data leakage.
A Hong Kong finance worker was tricked by a deepfake video call into wiring millions—now the same playbook is being industrialized with voice clones, synthetic meetings, and targeted social engineering. Which sectors are most exposed, and which controls actually break the fraud chain before money moves?
Role-playing, token manipulation, and many-shot prompting can steer enterprise LLMs past intended safeguards—even when the model appears well-guarded. The real question is how security teams can detect these attacks early and reduce the risk before sensitive data or workflow controls are exposed.
Employees are pasting source code, customer records, and internal strategy into unauthorized AI tools—often before security even knows those tools exist. This post examines the real leakage paths, practical ways to detect shadow AI across SaaS, browsers, and endpoints, and the policies that reduce risk without blocking legitimate work.
Prompt injection isn’t just “bad input” — indirect attacks can hide inside webpages, emails, or documents and override an AI system’s instructions even when the prompt itself looks clean. This post breaks down why traditional sanitization fails and which defenses actually help today: sandboxing, output validation, and privilege separation.
For years, typos and awkward phrasing were the telltale signs of phishing. Large language models just eliminated that detection signal entirely.
Developers are shipping AI-generated code faster than ever. Security researchers are finding the vulnerabilities just as fast.
Autonomous AI agents can browse the web, run code, and call APIs on your behalf. Attackers have noticed — and are already exploiting them.
Discover how Google's A2A protocol standardizes secure agent-to-agent communication and the critical security considerations organizations must address.
Learn what the Model Context Protocol (MCP) is, how it enables AI systems to share context, and key security considerations organizations must address.
Learn how AI tools like ChatGPT can help security teams respond faster, reduce false positives, and streamline analysis.
Discover how AI models help organizations detect social engineering, analyze metadata, and block malicious links in real time.
Speed up ISO 27001 and SOC 2 risk assessments using AI-powered tools that analyze, score, and suggest treatments for risks.
Explore how machine learning and large language models help security teams make sense of massive log volumes and detect anomalies faster.
Learn how generative AI can automate security questionnaires, analyze third-party risks, and improve vendor onboarding.