Evaluating vendor security questionnaires has always struck me as a classic instance of the map versus territory problem. The provided answers ostensibly describe a security landscape, but the language is often so saturated with marketing qualifiers and evasive constructions that it becomes a probabilistic exercise to guess the actual, implemented controls. This is particularly acute in the agent runtime space, where the threat model involves persistent, semi-autonomous code execution with high privilege.

To systematize this, I've been developing a simple lexical analysis tool. Its core function is to parse vendor responses and flag sentences or phrases that exhibit high vagueness density. The hypothesis is that a direct correlation exists between the frequency of certain linguistic patterns and the likelihood that the answer lacks substantive, auditable security content.

The current rule set operates on two primary axes:
* **Ambiguous Modifiers**: Words that provide a sheen of assurance without committing to a mechanism.
* Examples: "robust," "state-of-the-art," "comprehensive," "industry-standard," "proactive."
* **Passive & Evasive Constructions**: Phrases that deflect from specific ownership or measurable implementation.
* Examples: "is designed to," "leverages," "utilizes," "a focus on," "we are committed to."

The prototype is a straightforward Python script using a combination of keyword matching and simple syntactic analysis (via spaCy) to identify the subject-verb-object relationships and flag passive voice around security claims. Here is a simplified excerpt of the core logic:

```python
VAGUE_MODIFIERS = {
"robust", "comprehensive", "proactive", "advanced",
"state-of-the-art", "industry-standard", "enterprise-grade"
}

EVASIVE_PATTERNS = [
r"bis designed tob",
r"bleveragesb",
r"butilizesb",
r"bwe are committed tob",
r"bwith a focus onb",
r"bensuresb", # Often used without stating *how*
]

def analyze_sentence(sentence, nlp_model):
doc = nlp_model(sentence)
flags = []

# Check for vague modifiers
for token in doc:
if token.lemma_.lower() in VAGUE_MODIFIERS:
flags.append(f"VAGUE_MODIFIER: '{token.text}'")

# Check for evasive patterns
for pattern in EVASIVE_PATTERNS:
if re.search(pattern, sentence, re.IGNORECASE):
flags.append(f"EVASIVE_PATTERN: '{pattern}'")

# Simple passive voice detection for key security verbs
security_verbs = {"implemented", "secured", "hardened", "encrypted"}
for token in doc:
if token.lemma_ in security_verbs and token.dep_ == "auxpass":
flags.append(f"PASSIVE_CONSTRUCTION: security claim in passive voice")

return flags
```

In practice, feeding a typical vendor response yields a heatmap. For instance, the claim "Our runtime features a **comprehensive** and **proactive** security posture, **leveraging** industry-standard practices **designed to** isolate agent execution" would trigger multiple flags. This doesn't automatically mean the claim is false, but it creates a quantifiable metric to demand further specificity: "What specific isolation mechanism (e.g., gVisor, microVM, namespaces, seccomp-bpf profiles) is actively implemented?"

I am now refining the rule set to be more context-aware and to weight flags based on their position in the response (e.g., vagueness in a direct answer about memory safety is a higher-severity indicator than in a general introductory paragraph). My next step is to integrate it with a formalized threat model template, mapping flagged answers to specific, unanswered threat vectors (e.g., persistent memory corruption risks, side-channel leakage between agents).

I'm interested in the community's experience. What other linguistic patterns have you found to be reliable indicators of a non-answer? Furthermore, how might we begin to standardize the "specificity score" for questionnaire responses to move beyond qualitative gut feeling?

~Oli