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AI THREAT LANDSCAPE

The attacks you need to worry about in 2026.

AI systems face adversarial attacks, prompt injections, and autonomous agent exploits that traditional cybersecurity controls were never designed to detect or prevent.

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AI-Specific Attack Categories
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ATTACK TAXONOMY

10 AI Threat Categories Every CISO Must Manage

Comprehensive coverage mapped to MITRE ATLAS™ and industry standards.

01

Prompt Injection

Malicious instructions embedded in direct user inputs or external data streams that hijack model behavior, override guardrails, or exfiltrate sensitive data.

CRITICAL
02

Model Manipulation

Adversarial inputs crafted to trigger misclassifications, skew underwriting weights, induce hallucinations, or evade fraud scoring algorithms.

CRITICAL
03

Agent Exploitation

Manipulating autonomous tool-calling agents to execute unauthorized API requests, database queries, financial transfers, or privilege escalations.

CRITICAL
04

AI Supply Chain Attacks

Compromised foundation weights, malicious fine-tuning datasets, vulnerable vector plugins, and poisoned open-source libraries.

CRITICAL
05

Data Leakage & PII Exposure

Foundation models inadvertently regurgitating confidential documents, personal financial records, API keys, or customer PII into output streams.

HIGH
06

Regulatory & Governance Risk

Non-compliance with RBI FREE-AI directives, CERT-In cybersecurity guidelines, and ISO/IEC 42001 standards, risking penalties and loss of trust.

HIGH
07

Unsafe Autonomous Actions

Autonomous agents operating in production without human-in-the-loop validation, triggering unreviewed commercial contracts or trades.

CRITICAL
08

Training Data Poisoning

Adversarial contamination of training or RAG corpora to embed persistent logic backdoors, bias, or deceptive behaviors into internal models.

HIGH
09

Insecure Output Handling

Blindly trusting raw LLM outputs to downstream SQL queries, shell commands, or browser parsers, resulting in severe injection flaws (XSS/SQLi).

HIGH
THREAT 10

Shadow AI & Unauthorized Model Sprawl

Employees and teams utilizing unapproved public GenAI tools, custom GPTs, and unverified third-party APIs with proprietary business data, bypassing enterprise perimeter boundaries and data governance policies.

EMERGING VECTOR
THE IMPACT

AI risks aren't theoretical.
They're already happening.

  • Financial losses & fraudulent payouts
  • Model downtime & service outage
  • Severe reputational damage
  • Loss of customer and investor trust
  • Regulatory penalties & audit findings
  • Unsafe autonomous actions & liability
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