ISECURION's AI-Augmented Red Team goes beyond traditional network and human-focused red teaming to test the attack paths that matter now - AI supply chain compromise, LLM and agent exploitation, deepfake-driven social engineering, and AI-accelerated reconnaissance. Delivered by CERT-In empanelled red team specialists with dedicated AI/ML security expertise, to enterprises across the United States, United Kingdom, Europe, the GCC, Singapore, Australia, and India - remotely or on-site. Available as a standalone engagement or as an add-on module to your existing VAPT/RTaaS retainer.
Tell us about your AI footprint and region, and get a customized module-by-module quote. We respond within 24 hours, wherever you're based.
Traditional red team engagements test human and network attack paths - phishing, network pivoting, physical access. But AI systems have introduced entirely new attack paths: malicious model/dataset references injected into ML pipelines, prompt injection against LLM-powered products, agents with tool-calling access steered into unintended actions, and AI-cloned voices used to bypass financial approval controls. These risks apply equally to enterprises in New York, London, Frankfurt, Dubai, Singapore, Sydney, or Bangalore.
ISECURION's AI-Augmented Red Team is the most differentiated addition to our RTaaS portfolio, delivered to clients across the United States, Europe, the GCC, Singapore, Australia, and India. It tests AI systems as the target (your ML pipeline, your LLM product), the weapon (deepfake vishing, AI-accelerated recon), or both.
Each of the four engagement modules combines deep functional testing of guardrails, approval gates, and detection pipelines with hands-on attack simulation - so you learn not just whether an attack is possible, but whether your controls actually catch it. Engagements are scoped to align with local regulatory and data protection requirements - GDPR in Europe, DPDP in India, and equivalent frameworks in the GCC, Singapore, and Australia.
Tests whether your ML training/inference pipeline can be compromised the way Hugging Face-class supply chain attacks worked in the wild
Direct and indirect prompt injection, jailbreaks, and tool-call hijacking against your own LLM products and internal agent deployments
Deepfake voice/video cloning used in controlled, consented vishing simulations to test whether financial approval controls actually hold
A board-level benchmark showing exactly how much faster and broader an AI-driven attacker's recon is compared to a manual pentester
Any organization with a live AI footprint - product-facing or internal, anywhere in the world - carries attack paths a traditional red team won't cover
Customer-facing or internal products built on LLMs, exposed to direct prompt injection and jailbreak attempts
Agents with email, code execution, or payment tool-calling capability that could be hijacked into unintended actions
Teams operating model-training or inference pipelines, model registries, and CI/CD integrations vulnerable to supply chain attacks
Organizations where wire transfer or credential reset approvals depend on voice or video confirmation from executives
SOC teams that need to validate whether their monitoring catches AI-native attack patterns or treats them as background noise
Leadership across regions seeking a concrete, benchmarked answer to "how exposed are we to AI-driven attacks, and how fast is the threat moving?"
Run one module or the full suite. Each module can be scoped standalone or bundled at a discount
Reproduces the Hugging Face-class attack chain against your own ML pipeline in a client-approved staging environment - attempting to introduce a malicious dataset/model reference, testing whether processing workers hold over-scoped cloud/cluster credentials, attempting lateral movement into internal clusters, and testing whether your monitoring catches thousands of automated actions across short-lived sandboxes or treats it as noise.
Targets your own LLM-powered products or internal agent deployments: direct and indirect prompt injection, jailbreak attempts against safety guardrails, tool-call/function-calling hijack attempts, system prompt/confidential instruction extraction, and excessive-agency testing to see whether the agent performs high-risk actions without human approval gates.
Tests whether financial and executive-approval controls survive an AI-cloned-voice attack, using a voice clone of a consenting executive (or a synthetic persona) in simulated vishing calls requesting a wire transfer or credential reset. Specifically tests out-of-band verification protocols - does staff call back on a known number, or comply with the on-call instruction?
Runs ISECURION's own agentic recon tooling against your approved external footprint and compares time-to-first-finding and breadth of coverage against a manual pentester baseline - a strong board-level "why this matters now" artifact as much as a technical finding.
End-to-end attack simulation and functional validation across all four modules, for clients anywhere in the world
Attempted introduction of a malicious dataset/model reference into your training or inference pipeline, and credential-scoping tests on processing workers, in a client-approved staging environment only
Direct injection against client-facing chatbots/copilots, plus indirect injection via poisoned documents, support tickets, or webpages an AI browsing agent is expected to ingest
Attempts against safety guardrails using known bypass technique families, rated for severity and mapped to your specific LLM stack
Attempts to steer an agent with tool access - email, code execution, payments - into unintended actions outside its authorized scope
Testing whether confidential system instructions, internal policies, or proprietary prompt engineering can be extracted through conversation
Verifying whether the agent performs high-risk actions without human approval gates, and whether escalation thresholds are correctly enforced
Consented voice-clone vishing calls to finance/ops staff, testing out-of-band verification protocols and susceptibility to on-call wire transfer instructions
Agentic recon tooling run against your approved external footprint, benchmarked against a manual pentester baseline for time-to-first-finding and coverage
Coordinated (or pure red team) testing of whether your blue team catches each attack, how fast, and whether it's distinguishable from background noise
Attack simulation alone isn't enough - we validate whether your controls, gates, and detection pipelines actually function as designed.
| Functional Flow | What We Test | Security Checks | Included |
|---|---|---|---|
| Guardrail Response Consistency | Whether the LLM's safety guardrails respond consistently across repeated and rephrased prompts | Guardrail bypass via rephrasing, encoding tricks, multi-turn erosion | ✔ |
| Tool-Call Authorization Enforcement | Whether tool/function-calling permissions are correctly scoped per agent role and session | Privilege escalation via tool-call chaining, unauthorized tool invocation | ✔ |
| Human-in-the-Loop Approval Gates | Whether high-risk actions (payments, data deletion, external sends) correctly pause for human approval | Approval-gate bypass, silent auto-approval under load or ambiguity | ✔ |
| Credential Scoping for Processing Workers | Whether ML pipeline workers hold least-privilege cloud/cluster credentials | Over-scoped IAM roles, lateral movement from a compromised worker | ✔ |
| Detection & Alerting Pipeline | Whether automated, high-volume AI-driven actions trigger alerts or blend into normal traffic | Alert-fatigue exploitation, sandbox-noise camouflage, delayed detection | ✔ |
| Escalation Workflow | Whether flagged anomalies are actually routed to and acted on by the right on-call team, across time zones | Escalation-path gaps, stale contact lists, unacknowledged alerts | ✔ |
| Out-of-Band Verification Protocol | Whether finance/ops staff follow callback-on-known-number procedure for high-risk voice instructions | Vishing compliance rate, protocol bypass under urgency framing | ✔ |
| Model / Dataset Provenance Checks | Whether the pipeline validates the source and integrity of models/datasets before ingestion | Malicious model/dataset injection, unsigned artifact acceptance | ✔ |
| Session / Context Isolation | Whether one user's or tenant's conversation context can leak into or influence another's session | Cross-session context leakage, memory/context poisoning | ✔ |
| Error Handling & Fallback Logic | Agent/pipeline behavior under malformed input, timeouts, and partial failures | Verbose error disclosure, unsafe fallback defaults, stack trace exposure | ✔ |
Functional test evidence is packaged alongside the security findings in every module's final report - giving you a clear pass/fail view of both correctness and resilience, regardless of your region.
A structured process applied consistently across all four modules, adapted to your region's legal and regulatory context
Define target systems, staging vs. production boundaries, legal sign-off for social engineering/deepfake elements (adapted to local law - GDPR, DPDP, or GCC/APAC equivalents), and escalation contacts. Select modules based on your AI footprint.
Passive and active recon, including AI-specific footprint mapping - public model repos, exposed inference endpoints, and ML pipeline CI/CD references relevant to your organization.
Run the module-specific attack simulation - supply chain compromise attempt, LLM/agent red teaming, deepfake vishing calls, or AI-accelerated recon benchmark - per the agreed scope, coordinated remotely or on-site across your time zone.
Coordinate with your blue team (or run as a pure red team, per your preference) to test whether each attack was caught, and how fast.
Report all functional gaps and security findings with clear evidence, severity ratings, and step-by-step remediation guidance mapped to your specific AI stack.
Validate fixes post-remediation and issue a formal closure confirmation for remediated findings.
Deliver the complete attack narrative, per-technique pass/fail matrix, detection-gap report, and remediation roadmap - along with an executive summary suitable for board-level "why this matters now" conversations, delivered on a call at a time that works for your region.
Technical depth for your engineering team, and a clear narrative for your board - in your local currency and time zone
Step-by-step narrative of how the supply chain attack simulation unfolded, credential-scoping findings, and a detection-gap report
Every prompt injection, jailbreak, and tool-call hijack technique tested, with transcript evidence and guardrail-bypass severity ratings
Susceptibility rate and transcript/recording evidence from deepfake vishing calls, with consent and legal sign-off fully documented
Comparative timeline report showing what an AI-driven attacker would have found faster or differently versus a manual pentester baseline
Prioritized, developer-friendly remediation recommendations across all tested modules, feeding directly into follow-on hardening work
A concise, non-technical summary of exposure, findings, and recommended next steps - built for board-level AI threat-readiness conversations in any region
AI-forward companies across the US, Europe, GCC, Singapore, Australia, and India trust ISECURION to test what traditional red teams don't cover
Pair AI-Augmented Red Team with these complementary ISECURION services
Common questions from security and engineering teams across the United States, Europe, GCC, Singapore, Australia, and India
Partner with ISECURION - CERT-In empanelled, ISO 27001:2022 certified - for AI-Augmented Red Team testing that goes where traditional red teams don't.
Serving AI-forward enterprises, product teams & financial institutions across the United States, United Kingdom, Europe, GCC, Singapore, Australia and India.