AI/ML Security Assessment
You shipped an LLM feature; attackers shipped prompt injections. We test AI systems the way adversaries do — injection, jailbreaks, data leakage, tool abuse, poisoning paths — across the full stack: the model integration, the pipeline that feeds it, and the application around it.
Verified Solvex Specialist
Verified by SolvexDirect specialist contact for Solvex engagements
What this means. An authorized Solvex administrator registered and approved this exact public identity. What it does not. Solvex has not inspected the account on the platform, and this is not the platform's own verification.
Solvex specialists never ask for your passwords, recovery phrases, one-time codes, or payments to a personal account. Work, scope and invoices are agreed in writing through the official channels on this site.
Signed authorization required. This engagement is performed only against systems you own or are contractually authorized to have tested, under an agreed scope. See the responsible testing policy.
What's covered
- Prompt-injection testing: direct, indirect (via retrieved content) and cross-user
- Jailbreak and guardrail-bypass resistance of your system prompts and filters
- Sensitive-data leakage: system prompt extraction, training/context data exposure
- LLM tool-use and agent abuse: escalation through connected functions and APIs
- RAG pipeline security: retrieval poisoning, embedding-store access controls
- ML pipeline review: training-data handling, model registry, deployment integrity
What you receive
- Technical report with working attack transcripts as evidence
- OWASP LLM Top 10-mapped findings with severity and business impact
- Guardrail and architecture recommendations that survive model upgrades
- Free retest of remediated findings
- A red-team prompt suite your team can re-run against future releases
Evidence and reporting
How the work is kept honest- Evidence, frozen at issuanceFindings tie to something observed. When the report is issued, the evidence behind it is frozen in the same transaction and cannot be edited afterwards.
- A signed reportAn Ed25519 signature covers both the report content and the delivered file. Alter a byte of either and verification fails.
- Signed scope firstTesting starts only after written scope and signed authorization for systems you own or are entitled to have tested.
Anyone holding a Solvex report can verify it publicly without seeing its contents.
Our boundaries
What this engagement does not do, stated before it starts.
- Testing only against AI systems you own or are authorised to assess
- No attacks against the underlying foundation-model provider's infrastructure
- No use of extracted data beyond proof of impact
- Model alignment research and safety benchmarking are out of scope — this is applied security testing
How this engagement runs
- 01
Intake
Tell us the system, the goal and the constraints. If the work is not a good fit, we say so before anyone is invoiced.
- 02
Scope and authorization
Written scope and signed authorization before anything is touched. Security testing runs only against systems you own or are contractually entitled to have tested.
- 03
Investigation or build
Specialists matched to the work. Findings are proven by hand — scanner output is a lead, never a finding.
- 04
Evidence
Every finding ties to something observed. When a report is issued, its evidence is frozen in the same transaction, so what backed the report cannot change afterwards.
- 05
Delivery
A signed report: an Ed25519 signature over both the content and the file, with a short verification reference you can read down a phone.
- 06
Verification and retest
Anyone holding the report can verify it publicly without seeing its contents. Fixes are retested as part of the engagement — “fixed” means we confirmed it.
Questions we are asked
- What does an AI security assessment cover that a normal pentest doesn't?
- The attack classes unique to AI systems: prompt injection through every content channel, guardrail bypasses, system-prompt and context extraction, abuse of the tools an agent can call, and poisoning of retrieval pipelines. A standard web pentest touches none of these systematically — and they're where AI features actually fail.
- We use a major provider's model. Doesn't their safety work cover us?
- Provider-side safety reduces harmful generations; it does not secure your integration. Your system prompt, your retrieval pipeline, your tool wiring and your data boundaries are all yours — and that's where the exploitable behaviour lives. Provider safety and application security are complementary, not interchangeable.
- Will findings still matter after we upgrade models?
- Yes — that's why recommendations focus on architecture: privilege boundaries around tool calls, retrieval trust separation, and output handling. Model-specific jailbreak strings age quickly; architectural fixes don't, and the included prompt suite lets you re-verify after every upgrade.
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