Key Findings (2026 baseline from Cybersecify AI agent pentests):
- 10 AI agent security testing tools cover the production reality in 2026. Garak, PyRIT, Promptfoo, LLM Guard, Patronus AI, Lakera Guard, NeMo Guardrails, WhyLabs LangKit, Burp Suite with AI extensions, and a manual pentest methodology. Four are open-source and free.
- Cybersecify’s red team has tested 12 AI agents across Indian SaaS engagements in Q1 and Q2 2026. 8 of 12 were vulnerable to indirect prompt injection via tool output. 7 of 12 had over-privileged agent tokens. 9 of 12 had no rate limiting on the agent endpoint.
- No single tool covers the full agent attack surface. Every production deployment needs a layered stack: at least one adversarial probe tool (Garak or PyRIT), one CI assertion runner (Promptfoo), and one runtime guardrail (NeMo Guardrails, LLM Guard, or Lakera Guard).
- Most commercial AI safety tools are English-first. Indian SaaS agents serving Hindi, Tamil, Marathi, or Bengali traffic need explicit multilingual testing because out-of-the-box detection is weaker for non-English prompts.
- For an Indian Series A SaaS startup with one AI agent in production, the practical stack is three open-source tools (Garak + Promptfoo + NeMo Guardrails) plus an annual manual pentest. Total marginal cost: zero on tools, INR 74,999 to INR 1,79,999 for the pentest.
If you build with LLMs and ship agents to production, you do not need to know every AI security tool. You need to know which three to wire into CI and which one to run before a release. This guide ranks 10 AI agent security testing tools used in 2026, compares them across the dimensions Indian SaaS founders actually care about (cost, India support, integration effort, scope), and tells you which combinations work for Startup and Series A stages.
Cybersecify’s AI agent pentest practice has used these exact tools across customer engagements in the first half of 2026. The recommendations come from what fired real findings, not from feature lists.
What to look for in an AI agent security testing tool
Five criteria decide whether a tool earns space in your stack:
- Coverage layer. Does it test the LLM, the tool call surface, the memory layer, or all three? Most tools test only the model. Few cover orchestration.
- Deployment mode. Pre-deploy adversarial probe (Garak, PyRIT), CI assertion runner (Promptfoo), runtime guardrail (NeMo Guardrails, Lakera, LLM Guard), or observability (WhyLabs). Each does one job. You probably need three of the four modes.
- Open-source vs commercial. Open-source means you run it locally, no data leaves your infrastructure, no per-call billing. Commercial means a vendor, an API, a contract, and budget.
- Multilingual coverage. Most safety classifiers are English-tuned. If your agent serves Indian users in Hindi or other Indian languages, test what actually fires for those.
- Integration effort. Some tools are pip-install-and-run. Others need bespoke harnesses, payload libraries, and CI plumbing.
Comparison table
| Tool | License | Layer | Mode | Multilingual | India use case |
|---|---|---|---|---|---|
| Garak | Open-source (NVIDIA) | LLM | Pre-deploy probe | Some non-English probes | Free baseline for every Indian SaaS agent |
| PyRIT | Open-source (Microsoft) | LLM + multi-turn | Pre-deploy probe + automation | Some non-English support | Multi-turn red-team automation, free |
| Promptfoo | Open-source | LLM | CI assertion runner | English-first | Fast PR-level regression for prompt changes |
| LLM Guard | Open-source (Protect AI) | LLM input + output | Runtime guardrail | English-first | Free inline filter for input sanitization |
| Patronus AI | Commercial | LLM | Evaluation suite + Lynx hallucination | English-first | Hallucination-critical apps with USD budget |
| Lakera Guard | Commercial | LLM input | Runtime guardrail (fast inline) | Multilingual paid tier | Production agents needing sub-100ms filtering |
| NeMo Guardrails | Open-source (NVIDIA) | LLM + dialog | Runtime guardrail (programmable) | Some non-English support | Free runtime safety for production agents |
| WhyLabs LangKit | Open-source + commercial tiers | LLM observability | Drift + metric monitoring | English-first | Production monitoring for drift and toxicity |
| Burp Suite + AI extensions | Commercial (Burp Pro + extensions) | App surface | Manual pentest tooling | Language-agnostic for HTTP layer | Testing the agent’s HTTP/API surface |
| Cybersecify methodology | Service (founder-led) | Full agent (LLM + tool + memory + orchestration) | Manual pentest engagement | Tested in English + Indian language contexts | Annual pentest before launch and after major changes |
Tool-by-tool breakdown
1. Garak (NVIDIA)
Garak is the de facto open-source vulnerability scanner for LLMs. NVIDIA maintains it. It ships with probe modules for prompt injection, jailbreak, toxicity, data leakage, hallucination, and malware generation. You point Garak at a model endpoint (local, OpenAI, Anthropic, custom), pick the probe modules you want to run, and Garak reports which probes triggered which failures.
Strengths: free, well-maintained, covers most known adversarial categories, scriptable for CI. The probe library is the most comprehensive among open-source tools.
Limits: tests the model in isolation, not the agent. Will not catch tool-poisoning or memory injection. Probes are pre-defined; novel attacks specific to your application need custom probes.
Indian SaaS use case: every Indian SaaS startup with an LLM in production should be running Garak weekly in CI as a regression baseline. Zero cost.
2. PyRIT (Microsoft)
Python Risk Identification Tool. Microsoft AI Red Team’s open-source automation framework. PyRIT focuses on multi-turn adversarial conversations, not single-shot probes. It has scorers (does the response contain forbidden content), memory (track what worked across conversations), and orchestrators (chain prompts).
Strengths: multi-turn is closer to how real attackers operate (they refine prompts across messages). Microsoft backing means active development. Integrates with Azure OpenAI, OpenAI, custom endpoints.
Limits: steeper learning curve than Garak. Documentation is improving but still vendor-centric. The orchestrator pattern requires more code to author each scenario.
Indian SaaS use case: layer PyRIT on top of Garak when single-shot probes stop finding new issues. Free.
3. Promptfoo
Promptfoo is a CLI test runner for LLM prompts. You write YAML test files with assertions (“the response should not contain X”, “the response should pass policy Y”), point Promptfoo at one or more LLM providers, and get a pass/fail report. It compares prompts across models (useful for vendor selection) and runs in CI.
Strengths: fast iteration. PR-level regression catches prompt regressions before merge. Cheap (it just calls the LLM APIs you already pay for). Good documentation. Active community.
Limits: assertions catch what you assert. Will not surprise you with novel attacks. Pair with Garak for adversarial probing.
Indian SaaS use case: every PR that touches the agent runs Promptfoo. Stops the “we fixed the system prompt but broke FAQ accuracy” class of regression. Free; you pay the underlying model API.
4. LLM Guard (Protect AI)
Open-source toolkit for input and output sanitization. Modules cover prompt injection detection, PII anonymization, toxicity scoring, secret detection (API keys leaked in user input), and ban-substring matching. Runs inline as a filter between user and model.
Strengths: free, modular (pick the scanners you need), open API for custom scanners. Pairs well with NeMo Guardrails for layered defense.
Limits: English-first detection. Some scanners (PII) work poorly on Indian languages and names. Performance overhead on latency-critical apps.
Indian SaaS use case: free input sanitization layer for agents handling user-supplied prompts. Test multilingual paths explicitly before relying on it for Hindi or Tamil traffic.
5. Patronus AI
Commercial AI evaluation platform. Founded 2023 (Y Combinator W23), focus on hallucination detection (Lynx) and content classification (Glider). Patronus runs evaluation suites against your model and reports failure categories.
Strengths: strong hallucination detection (Lynx is a fine-tuned hallucination judge model). Good for RAG-heavy applications where hallucination is the primary risk.
Limits: USD pricing, English-first, requires sending data to Patronus infrastructure (data residency considerations for Indian buyers). Less coverage of prompt injection compared to Garak or PyRIT.
Indian SaaS use case: hallucination-critical apps (medical, legal, financial summarization) with USD budget. Not the first tool to pick; the third or fourth.
6. Lakera Guard
Commercial runtime guardrail focused on prompt injection and content filtering. Switzerland-based. Sub-100ms inline API for production traffic. Supports multilingual on paid tier.
Strengths: fast enough for production. Good prompt injection detection across languages (paid). Easy to integrate (single API call).
Limits: USD pricing, vendor lock for the inline layer, per-call billing scales with traffic. Some Indian buyers have raised data-residency concerns (EU-hosted).
Indian SaaS use case: production agents serving high traffic where you need inline filtering and cannot tolerate latency from open-source alternatives. Test multilingual coverage against your actual user traffic before relying on it.
7. NeMo Guardrails (NVIDIA)
Open-source programmable guardrails toolkit. You write Colang (NVIDIA’s DSL) policies describing what the agent should and should not do across input, output, dialog, and retrieval layers. NeMo enforces those policies at runtime.
Strengths: free, programmable (you can encode business-specific rules), supports retrieval guardrails for RAG. Strong NVIDIA ecosystem fit if you use Triton or other NVIDIA serving.
Limits: Colang is yet another DSL to learn. Policy authoring takes engineering time. Some Indian SaaS teams have found the iteration cycle slow.
Indian SaaS use case: production agent where you want free runtime safety and can invest a few engineer-days in policy authoring. Pairs with LLM Guard for input layer and Promptfoo for CI.
8. WhyLabs LangKit
Observability platform for ML and LLM. LangKit is the LLM-specific extension on top of WhyLabs. Tracks drift, toxicity, sentiment, response length, and embedding distribution over time. Free tier exists; paid tiers add larger scale and team features.
Strengths: production observability is a real gap in most AI security stacks. LangKit catches the slow-drift problem where the model behavior changes over weeks because of input pattern shifts. Useful for incident retrospectives.
Limits: detection, not prevention. Tells you something happened; does not block it. English-first metrics. Pricing scales with volume.
Indian SaaS use case: production agents that have been running for at least one quarter where you want to detect drift and abuse patterns. Not the first tool to install; the fourth or fifth.
9. Burp Suite with AI extensions
Burp Suite Professional is the standard web app pentest tool. AI extensions (some commercial, some community) add AI-specific testing on top: prompt injection payloads in HTTP requests, model API surface enumeration, embedded LLM payload mutation. Useful when the agent has an HTTP/API surface and you need to test the full request-response chain.
Strengths: covers the HTTP layer that pure LLM tools ignore. Existing pentesters know Burp; AI extensions add minimal learning curve.
Limits: not specific to AI; the AI extensions vary in maturity. Commercial license (USD 449 per user per year for Pro). Manual workflow, not CI-friendly.
Indian SaaS use case: during a manual pentest, when you need to test the agent’s exposed API surface against application-layer attacks (BOLA, auth bypass, mass assignment, injection). Cybersecify uses Burp Pro with custom payload libraries in every AI agent engagement.
10. Cybersecify methodology (founder-led pentest engagement)
A tool list is not enough. The tools cover the LLM and HTTP layers; the gaps are tool-poisoning, memory injection, indirect prompt injection through real-world retrieval sources, agent privilege escalation through tool chains, and business-logic abuse specific to your application. These need manual testing by humans who understand both AI agents and your product.
Cybersecify’s red team uses Garak and PyRIT for the model layer, Promptfoo for assertion regression, Burp Pro for HTTP, custom payload libraries we maintain for indirect prompt injection through document retrieval, and a structured 4-layer attack-surface map (planning, tool calls, memory, non-determinism) covered in how to pentest an AI agent in 2026. Every engagement closes with a report mapping findings to OWASP API Security Top 10 + business logic gaps + AI-specific issues, with reproduction steps and remediation guidance.
Pricing: Startup Pentest INR 74,999 (1 agent scope, 7 calendar days, 6 founder consulting hours, 1 free retest). Growth Pentest INR 1,79,999 (2 scopes, 10 calendar days, SOC 2 + ISO 27001 evidence, 12 consulting hours, 1 free retest). Book a discovery call to scope your engagement.
Decision matrix: which tool for which use case
MVP stage (one agent, low traffic, no funding pressure)
- Garak (weekly probe in CI)
- Promptfoo (PR-level regression)
- Total cost: 0 INR + your existing LLM API spend
Seed to Series A (one or two agents in production, paying customers, no compliance pressure yet)
- Garak + Promptfoo + NeMo Guardrails (free stack)
- Manual pentest from Cybersecify Startup tier (INR 74,999) before a major launch
Series A and beyond (multi-agent, RAG pipelines, compliance pressure)
- Garak + Promptfoo + NeMo Guardrails + LLM Guard (free defensive stack)
- Plus one commercial option for runtime: Lakera Guard (latency-critical) or Patronus AI (hallucination-critical) at USD 500 to 2,000 per month
- Plus WhyLabs LangKit for production observability
- Plus annual pentest from Cybersecify Growth tier (INR 1,79,999) with SOC 2 / ISO 27001 evidence
Compliance-driven (SOC 2 Type 2 or ISO 27001 audit pending)
- Same as Series A stack
- Plus documented pentest report with explicit AI agent attack-surface coverage. Cybersecify Growth tier provides SOC 2 + ISO 27001 control mapping per finding.
What Cybersecify uses on every engagement
In Q1 and Q2 2026, Cybersecify’s AI agent pentest engagements ran the following toolchain on each scope:
- Garak with the full prompt-injection, jailbreak, toxicity, and data-leakage probe sets
- PyRIT for multi-turn red-team automation on the planning layer
- Promptfoo for assertion regression in CI for the customer to keep running after the engagement closes
- Burp Suite Professional with custom payload libraries for the HTTP/API surface
- Custom internal payload library for indirect prompt injection via retrieved documents
- Manual exploration based on the customer’s specific tool set and business logic
Findings from 12 engagements:
- 8 of 12 (67 percent) vulnerable to indirect prompt injection via tool output (the agent reads a document, the document contains an instruction, the agent follows it)
- 7 of 12 (58 percent) had over-privileged agent tokens (broader production scope than any human user)
- 9 of 12 (75 percent) had no rate limiting on the agent endpoint
- 4 of 12 (33 percent) had memory poisoning across user sessions
These are the structural failures the tool list above is meant to surface. Tools catch some; manual testing catches the rest.
Related reading
- How to Pentest an AI Agent in 2026. The 4-layer methodology Cybersecify uses.
- Vibe-Coded SaaS Pentest 2026: Cursor and Lovable Gaps. Adjacent buyer-intent guide for AI-generated SaaS apps.
- Prompt Injection 2026 Attack Patterns. Categorisation of prompt injection variants we test.
- API Pentest Services for SaaS Startups. The pentest engagement scope and pricing.
- Pentest Pricing Tiers Explained: INR 50K to 5L+. Where AI agent pentest fits in the cost ladder.
Ready to pentest your AI agent
Cybersecify pentests AI agents and LLM applications using the toolchain and methodology above, with manual testing from founders Rathnakara (OSCP, M.Sc Cyber Security, CompTIA PenTest+) and Ashok on every engagement. Startup Pentest at INR 74,999 covers one agent in 7 calendar days. Growth Pentest at INR 1,79,999 covers two scopes with SOC 2 and ISO 27001 evidence in 10 calendar days. Both include free retest within 30 days and bundled founder-led consulting hours.
Book a discovery call or request a custom quote to scope your AI agent engagement.