A recent wave of security startups market themselves as 'autonomous AI agents' that plug into your OpenAI or Anthropic API keys. While Large Language Models are remarkable at summarizing text and drafting code, using them as primary automated exploitation engines introduces severe technical flaws.
The dangers of LLM hallucinations in vulnerability assessment
LLMs are probabilistic token predictors, not formal state machines. When instructed to find vulnerabilities, an LLM often exhibits confirmation bias—hallucinating that an API endpoint leaked sensitive data when it merely returned a standard 404 response.
- Phantom vulnerabilities: LLM agents frequently claim to have discovered SQL injection or remote code execution based on plausible-sounding but completely fictitious reasoning.
- Runaway API billing: multi-step agent loops making thousands of LLM API calls can run up hundreds of dollars in OpenAI token costs for a single assessment run.
- Confidential data exposure: piping entire HTTP request/response payloads to third-party commercial LLM providers can inadvertently violate customer privacy agreements.
- Non-reproducible testing: the non-deterministic nature of temperature-based models means running the scan twice against the exact same target yields two completely different sets of results.
The BugSnaps deterministic approach
BugSnaps MyPentest operates without requiring you to supply personal AI model keys. Our checks are deterministic, reproducible, and verifiable. If a test detects an issue, it generates an immutable HTTP evidence trace that any developer can reproduce with a standard curl command.
Security testing demands mathematical certainty, not probabilistic guesses. BugSnaps guarantees reproducible evidence with zero personal model costs and zero hallucinations.