alphaear-reporter
Pass
Audited by Gen Agent Trust Hub on May 7, 2026
Risk Level: SAFEPROMPT_INJECTION
Full Analysis
- [PROMPT_INJECTION]: The skill is susceptible to indirect prompt injection (Category 8) because it fetches and processes data from external news APIs and web search results. Maliciously crafted data from these sources could attempt to influence the agent's behavior or override its internal logic. The skill uses boundary markers to separate external content from instructions, which provides a degree of protection but does not eliminate the risk from sophisticated adversarial data.
- Ingestion points:
scripts/utils/content_extractor.py(fetches web content via Jina Reader API),scripts/utils/news_tools.py(fetches news from the NewsNow API). - Boundary markers: Present in
scripts/prompts/fin_agent.py(e.g.,=== 原始信号 ===) andscripts/prompts/report_agent.pyto delimit untrusted data. - Capability inventory: The agent can search the web, fetch real-time news, read and write to a local SQLite database (
data/signal_flux.db), and generate HTML charts. - Sanitization: Implements robust JSON extraction and comment stripping in
scripts/utils/json_utils.py, though it lacks explicit sanitization to filter out prompt-injection patterns from external text. - [EXTERNAL_DOWNLOADS]: The skill fetches data and software components from several well-known services. These are legitimate operations consistent with the skill's purpose.
- Financial Data: Fetches stock lists and historical prices using the
aksharelibrary, which interacts with public financial endpoints. - News & Sentiment: Connects to
newsnow.busiyi.worldfor real-time news andgamma-api.polymarket.comfor prediction market data. - Models: Downloads pre-trained models from Hugging Face, including
sentence-transformersfor embeddings and BERT-based models for sentiment analysis. - [COMMAND_EXECUTION]: The skill executes Python scripts for data visualization and model training. Static analysis hints regarding
eval()calls were found to be false positives, as they refer to PyTorch'smodel.eval()method for setting evaluation mode andast.literal_eval()for safe string-to-dictionary conversion.
Audit Metadata