Pen-Testing AI Agents: What a CISO should ask for — and how to read the report
PRISM Strike — AI Adversarial Testing & Red Teaming for Models, Apps & Agents
Test your models, GenAI applications, and agents against adversarial attacks, prompt injections, and safety bypasses—before they hit production. Every run draws on a 15,000+ attack corpus that grows daily and a live mutation engine, so a static defense can’t pattern-match its way to a pass.
WHY IT MATTERS
As AI models, applications, and agents move from experimentation to production, traditional testing methods produce findings without exploit proof and are not designed to uncover how they behave under hostile inputs, leaving critical risks unaddressed. The risk peaks where an agent can act — reaching tools, customer or payment data — where a single exploited weakness executes at machine speed.
Findings Without Exploit Proof
Detection-style tools and runtime LLM firewalls flag suspicion. Prompt injection attack simulation with replayable exploit chains is what auditors and boards demand - not assumptions.
Language-Coverage-Gap Attacks
LLM safety training concentrates in English. Prompt injection and jailbreak techniques that fail in English often succeed in Bengali, Swahili, Vietnamese, Thai.
Point-in-Time Engagements
Manual red teams and consulting reports are non-repeatable, not pipeline-integrated, and economically impractical for continuous testing.
Multi-agent offensive generation
Most of the open-source LLM testing tools offer no quantitative breakage rate with reproducibility validation, no progressive L1–L4 severity tiering, and no multi-agent offensive generation.
Excessive focus on detection and classification
Commercial AI red teaming platforms focus on detection or classification rather than full OWASP LLM Top 10 coverage and few provide sequential L1–L4 severity progression mapping vulnerability to business risk.
Apps, Agents & Guardrails Go Live Untested
GenAI applications ship faster than annual pen-tests can cover, agents that can act are deployed with no way to prove they can’t be manipulated into an unauthorized action, and guardrails are logged as ‘in place’ with no evidence they actually block an attack.
Our Approach
PRISM Strike adopts a multilingual 4-step approach to simulate real-world threats and validates your models, applications and agents’ safety posture based on the OWASP AI Testing Guide, mapped to the OWASP LLM Top 10 (2025) and OWASP Agentic Top 10.
Generate
AI-vs-AI offensive engine creates persona-driven adversaries with mutation and transferability testing across LLM architectures. Powered by SISA’s own malicious LLM and 20 years of payment-application testing DNA, so it attacks the way an adversary who understands banking actually thinks.
Attack
Thousands of curated prompts from a 15,000+ case corpus that grows daily across 10 categories, 62 techniques, 25 evasion tactics — in 10+ high- and low-resource languages. Executed against models, GenAI applications and agents — probing prompt injection, tool chains and excessive agency, and validating deployed guardrails.
Measure
Quantitative Breakage Rate with reproducibility validation. V-Score (0–10) and multi-dimensional risk: Exploitability, Impact, Severity, Business Risk Index. Severity rolls up into PRISM Govern’s live risk score, so a control is scored on whether it blocked an attack, not on whether a box was ticked.
Evidence
Replayable exploit trails with engineering fix guidance. Pre-mapped to EU AI Act, NIST AI RMF, ISO 42001, MITRE ATLAS, the OWASP LLM Top 10, PCI DSS 4.0.1 and the payment-sector regulators (SAMA, CBUAE, RBI, MAS).
Service Offerings
PRISM Strike delivers a full suite of capabilities to find, measure, and prove AI system vulnerabilities with quantitative evidence.
10 attack categories, 62 attack techniques, 25 evasion tactics with thousands of curated and mutated prompts per run.
Testing is executed across both high-resource languages (English, Spanish, Mandarin, French, German, Arabic, Japanese, Russian) and low-resource languages (Hindi, Bengali, Swahili, Vietnamese, Thai, Indonesian, and others).
Four severity tiers (L1 Safe → L2 Guarded → L3 Exploitable → L4 Critical) applied in sequence that reveals not just whether a model fails, but precisely where its defenses degrade.
Measures the percentage of test cases that successfully breach security controls, with reproducibility validation and false-positive elimination.
Multi-agent attack creation with persona-driven adversaries simulating different attacker profiles, mutation and transferability testing across LLM architectures and context-aware exploit discovery.
V-Score (0–10) aligned with industry severity standards. Multi-dimensional scoring across Exploitability, Impact, Severity (CVSS-aligned), and Business Risk Index — connecting technical findings to business-level risk decisions.
Every finding includes evidence logs, the exact exploit chain, and engineering remediation guidance. Exploits are replayable for verification.
Connects to the application, maps its workflows, finds the AI-calling paths, and builds and mutates breaking scenarios against them — covering prompt injection and the AI attack surface as well as the underlying OWASP web flaws that annual, feature-only testing misses. AI-aware, not web-only.
Tests an agent through its API or app, learns its tools and goal, and builds agent-specific attack scenarios to prove whether it can be driven to an unauthorized action — built for agent goals, tool chains and excessive agency, before it can touch customer or payment data.
Red-teams the guardrails you have deployed and shows exactly which attacks they hold against — replacing a register entry that says ‘guardrail in place’ with executed evidence that it works.
Configurable testing frequency with continuous validation and regression re-test, so a model that drifts into vulnerability after release is caught rather than trusted on last year’s result.
BENEFITS
By testing models, applications and agents against real adversarial scenarios, PRISM Strike helps organizations strengthen resilience while maintaining performance and efficiency.
Quantifies AI vulnerability using Breakage Rate instead of pass/fail findings.
Surfaces language-coverage-gap vulnerabilities invisible to English-only testing.
Enables remediation prioritization based on real attack success rates and Business Risk Index.
Progressive L1–L4 severity reveals exactly where defenses degrade
Discovers attacks human testers miss using multi-agent attack creation with persona-driven adversaries, mutation, and transferability testing.
Replayable exploit trails offer audit-grade evidence for audit, governance, and board reporting.
Proves applications and agents can’t be driven off-task before they ship —including copilots and agents that can trigger refunds, move money, or touch payment data.
Turns testing into governed, scored risk: executed attack scenarios flow into PRISM Govern’s live risk score, so a control is scored on whether it actually blocked an attack — governance evidenced by execution, not attestation.
A 15,000+ attack corpus that grows daily, plus a live mutation engine, keeps coverage current and can’t be gamed by pre-training against a known set — evidence that survives regulator scrutiny.
Why Organizations Choose PRISM Strike from SISA
Our approach provides measurable outcomes and regulatory alignment to help organizations confidently test, assess and benchmark AI model, application and agent security.
Full OWASP LLM Top 10 + Agentic Coverage
10 attack categories, 62 techniques, 25 evasion tactics — plus OWASP Top 10 for Agentic AI.
Validated attack scenarios with documented evidence
Executes controlled adversarial tests with clear proof of how models respond under real attack conditions.
Quantitative severity, not binary pass/fail
Evaluates model behavior against defined test scenarios to provide objective, defensible results.
Baseline attack resilience metrics for benchmarking
Establishes measurable benchmarks to track and improve model resilience
over time.
Regulatory alignment with leading AI frameworks
Maps results to standards such as ISO/IEC 42001, EU AI Act (Article 40), the OWASP LLM Top 10, PCI DSS 4.0.1, the payment-sector regulators (SAMA, CBUAE, RBI, MAS), and HITRUST AI Security Assessment.
Twenty Years of Payment-Application DNA
PRISM Strike’s threat and risk scenarios are fed by two decades of SISA’s payment-application testing — business logic, real attack patterns, payment workflows — so it tests the way an attacker who understands banking actually thinks. Backed by SISA’s PCI QSA heritage.
A Corpus That Can’t Be Outdated or Outguessed
A 15,000+ attack library growing daily, plus a live mutation engine powered by SISA’s own malicious LLM — a genuine research investment no static red-teaming library matches.
Governance Evidenced by Execution — the Closed-Loop Moat
Executed attack scenarios flow into PRISM Govern’s live risk score, so a control is scored on whether it actually blocked an attack, not on whether a box was ticked — with evidence mapped to PCI DSS 4.0.1, SAMA, CBUAE, RBI and MAS.
Measure AI Risk. Don’t Assume It.
